Prognostic Significance of Tumor-associated Stroma in Nasopharyngeal Carcinoma: A Multicenter Study.

Assessment of tumor-associated stroma has shown a reliable prognostic value in recent research. We evaluated the prognostic value of tumor-stroma ratio (TSR) in a large multicenter cohort of nasopharyngeal carcinoma (NPC). We used the conventional hematoxylin and eosin-stained slides of 115 cases of NPC to assess TSR as described in recent guidelines. The amount of tumor-associated stroma was assessed as a percentage and then tumors were classified as stroma-high (>50%) or stroma-low (≤50%). Kaplan-Meier curves, χ 2 test, and Cox regression univariable and multivariable analyses were carried out. A total of 48 (41.7%) tumors were stroma-high and 67 (58.3%) tumors were stroma-low. In the Cox regression multivariable analysis, the tumors categorized as stroma-high were associated with a worse overall survival with a hazard ratio of 2.30 (95% CI: 1.27-4.15, P =0.006) and with poor disease-specific survival (hazard ratio=1.87, 95% CI: 1.07-3.28, P =0.029). The assessment of TSR in NPC is simple and cost-effective, and it has a significant prognostic value. TSR can aid in risk stratification and clinical decision-making in NPC.

Nuclei instance segmentation from histopathology images using Bayesian dropout based deep learning.

BACKGROUND: The deterministic deep learning models have achieved state-of-the-art performance in various medical image analysis tasks, including nuclei segmentation from histopathology images. The deterministic models focus on improving the model prediction accuracy without assessing the confidence in the predictions. METHODS: We propose a semantic segmentation model using Bayesian representation to segment nuclei from the histopathology images and to further quantify the epistemic uncertainty. We employ Bayesian approximation with Monte-Carlo (MC) dropout during the inference time to estimate the model's prediction uncertainty. RESULTS: We evaluate the performance of the proposed approach on the PanNuke dataset, which consists of 312 visual fields from 19 organ types. We compare the nuclei segmentation accuracy of our approach with that of a fully convolutional neural network, U-Net, SegNet, and the state-of-the-art Hover-net. We use F1-score and intersection over union (IoU) as the evaluation metrics. The proposed approach achieves a mean F1-score of 0.893 ± 0.008 and an IoU value of 0.868 ± 0.003 on the test set of the PanNuke dataset. These results outperform the Hover-net, which has a mean F1-score of 0.871 ± 0.010 and an IoU value of 0.840 ± 0.032. CONCLUSIONS: The proposed approach, which incorporates Bayesian representation and Monte-Carlo dropout, demonstrates superior performance in segmenting nuclei from histopathology images compared to existing models such as U-Net, SegNet, and Hover-net. By considering the epistemic uncertainty, our model provides a more reliable estimation of the prediction confidence. These findings highlight the potential of Bayesian deep learning for improving medical image analysis tasks and can contribute to the development of more accurate and reliable computer-aided diagnostic systems.

A FinnGen pilot clinical recall study for Alzheimer's disease.

Successful development of novel therapies requires that clinical trials are conducted in patient cohorts with the highest benefit-to-risk ratio. Population-based biobanks with comprehensive health and genetic data from large numbers of individuals hold promise to facilitate identification of trial participants, particularly when interventions need to start while symptoms are still mild, such as for Alzheimer's disease (AD). This study describes a process for clinical recall studies from FinnGen. We demonstrate the feasibility to systematically ascertain customized clinical data from FinnGen participants with ICD10 diagnosis of AD or mild cognitive disorder (MCD) in a single-center cross-sectional study testing blood-based biomarkers and cognitive functioning in-person, computer-based and remote. As a result, 19% (27/140) of a pre-specified FinnGen subcohort were successfully recalled and completed the study. Hospital records largely validated registry entries. For 8/12 MCD patients, other reasons than AD were identified as underlying diagnosis. Cognitive measures correlated across platforms, with highest consistencies for dementia screening (r = 0.818) and semantic fluency (r = 0.764), respectively, for in-person versus telephone-administered tests. Glial fibrillary acidic protein (GFAP) (p < 0.002) and phosphorylated-tau 181 (pTau-181) (p < 0.020) most reliably differentiated AD from MCD participants. We conclude that informative, customized clinical recall studies from FinnGen are feasible.

Expression profiles of small non-coding RNAs in breast cancer tumors characterize clinicopathological features and show prognostic and predictive potential.

Precision medicine approaches are required for more effective therapies for cancer. As small non-coding RNAs (sncRNAs) have recently been suggested as intriguing candidates for cancer biomarkers and have shown potential also as novel therapeutic targets, we aimed at profiling the non-miRNA sncRNAs in a large sample set to evaluate their role in invasive breast cancer (BC). We used small RNA sequencing and 195 fresh-frozen invasive BC and 22 benign breast tissue samples to identify significant associations of small nucleolar RNAs, small nuclear RNAs, and miscellaneous RNAs with the clinicopathological features and patient outcome of BC. Ninety-six and five sncRNAs significantly distinguished (Padj < 0.01) invasive local BC from benign breast tissue and metastasized BC from invasive local BC, respectively. Furthermore, 69 sncRNAs significantly associated (Padj < 0.01) with the tumor grade, hormone receptor status, subtype, and/or tumor histology. Additionally, 42 sncRNAs were observed as candidates for prognostic markers and 29 for predictive markers for radiotherapy and/or tamoxifen response (P < 0.05). We discovered the clinical relevance of sncRNAs from each studied RNA type. By introducing new sncRNA biomarker candidates for invasive BC and validating the potential of previously described ones, we have guided the way for further research that is warranted for providing novel insights into BC biology.

A genome-wide gene-based gene-environment interaction study of breast cancer in more than 90,000 women.

Background: Genome-wide association studies (GWAS) have identified more than 200 susceptibility loci for breast cancer, but these variants explain less than a fifth of the disease risk. Although gene-environment interactions have been proposed to account for some of the remaining heritability, few studies have empirically assessed this. Methods: We obtained genotype and risk factor data from 46,060 cases and 47,929 controls of European ancestry from population-based studies within the Breast Cancer Association Consortium (BCAC). We built gene expression prediction models for 4,864 genes with a significant (P<0.01) heritable component using the transcriptome and genotype data from the Genotype-Tissue Expression (GTEx) project. We leveraged predicted gene expression information to investigate the interactions between gene-centric genetic variation and 14 established risk factors in association with breast cancer risk, using a mixed-effects score test. Results: After adjusting for number of tests using Bonferroni correction, no interaction remained statistically significant. The strongest interaction observed was between the predicted expression of the C13orf45 gene and age at first full-term pregnancy (PGXE=4.44×10-6). Conclusion: In this transcriptome-informed genome-wide gene-environment interaction study of breast cancer, we found no strong support for the role of gene expression in modifying the associations between established risk factors and breast cancer risk. Impact: Our study suggests a limited role of gene-environment interactions in breast cancer risk.

Area-based breast percentage density estimation in mammograms using weight-adaptive multitask learning.

Breast density, which is a measure of the relative amount of fibroglandular tissue within the breast area, is one of the most important breast cancer risk factors. Accurate segmentation of fibroglandular tissues and breast area is crucial for computing the breast density. Semiautomatic and fully automatic computer-aided design tools have been developed to estimate the percentage of breast density in mammograms. However, the available approaches are usually limited to specific mammogram views and are inadequate for complete delineation of the pectoral muscle. These tools also perform poorly in cases of data variability and often require an experienced radiologist to adjust the segmentation threshold for fibroglandular tissue within the breast area. This study proposes a new deep learning architecture that automatically estimates the area-based breast percentage density from mammograms using a weight-adaptive multitask learning approach. The proposed approach simultaneously segments the breast and dense tissues and further estimates the breast percentage density. We evaluate the performance of the proposed model in both segmentation and density estimation on an independent evaluation set of 7500 craniocaudal and mediolateral oblique-view mammograms from Kuopio University Hospital, Finland. The proposed multitask segmentation approach outperforms and achieves average relative improvements of 2.88% and 9.78% in terms of F-score compared to the multitask U-net and a fully convolutional neural network, respectively. The estimated breast density values using our approach strongly correlate with radiologists' assessments with a Pearson's correlation of [Formula: see text] (95% confidence interval [0.89, 0.91]). We conclude that our approach greatly improves the segmentation accuracy of the breast area and dense tissues; thus, it can play a vital role in accurately computing the breast density. Our density estimation model considerably reduces the time and effort needed to estimate density values from mammograms by radiologists and therefore, decreases inter- and intra-reader variability.

Distinct Reproductive Risk Profiles for Intrinsic-Like Breast Cancer Subtypes: Pooled Analysis of Population-Based Studies.

BACKGROUND: Reproductive factors have been shown to be differentially associated with risk of estrogen receptor (ER)-positive and ER-negative breast cancer. However, their associations with intrinsic-like subtypes are less clear. METHODS: Analyses included up to 23 353 cases and 71 072 controls pooled from 31 population-based case-control or cohort studies in the Breast Cancer Association Consortium across 16 countries on 4 continents. Polytomous logistic regression was used to estimate the association between reproductive factors and risk of breast cancer by intrinsic-like subtypes (luminal A-like, luminal B-like, luminal B-HER2-like, HER2-enriched-like, and triple-negative breast cancer) and by invasiveness. All statistical tests were 2-sided. RESULTS: Compared with nulliparous women, parous women had a lower risk of luminal A-like, luminal B-like, luminal B-HER2-like, and HER2-enriched-like disease. This association was apparent only after approximately 10 years since last birth and became stronger with increasing time (odds ratio [OR] = 0.59, 95% confidence interval [CI] = 0.49 to 0.71; and OR = 0.36, 95% CI = 0.28 to 0.46 for multiparous women with luminal A-like tumors 20 to less than 25 years after last birth and 45 to less than 50 years after last birth, respectively). In contrast, parous women had a higher risk of triple-negative breast cancer right after their last birth (for multiparous women: OR = 3.12, 95% CI = 2.02 to 4.83) that was attenuated with time but persisted for decades (OR = 1.03, 95% CI = 0.79 to 1.34, for multiparous women 25 to less than 30 years after last birth). Older age at first birth (Pheterogeneity < .001 for triple-negative compared with luminal A-like breast cancer) and breastfeeding (Pheterogeneity < .001 for triple-negative compared with luminal A-like breast cancer) were associated with lower risk of triple-negative breast cancer but not with other disease subtypes. Younger age at menarche was associated with higher risk of all subtypes; older age at menopause was associated with higher risk of luminal A-like but not triple-negative breast cancer. Associations for in situ tumors were similar to luminal A-like. CONCLUSIONS: This large and comprehensive study demonstrates a distinct reproductive risk factor profile for triple-negative breast cancer compared with other subtypes, with implications for the understanding of disease etiology and risk prediction.

Circulating Cell-Free DNA Reflects the Clonal Evolution of Breast Cancer Tumors.

Liquid biopsy of cell-free DNA (cfDNA) is proposed as a potential method for the early detection of breast cancer (BC) metastases and following the clonal evolution of BC. Though the use of liquid biopsy is a widely discussed topic in the field, only a few studies have demonstrated such usage so far. We sequenced the DNA of matched primary tumor and metastatic sites together with the matched cfDNA samples from 18 Eastern Finnish BC patients and investigated how well cfDNA reflected the clonal evolution of BC interpreted from tumor DNA. On average, liquid biopsy detected 56.2 ± 7.2% of the somatic variants that were present either in the matched primary tumor or metastatic sites. Despite the high discordance observed between matched samples, liquid biopsy was found to reflect the clonal evolution of BC and identify novel driver variants and therapeutic targets absent from the tumor DNA. Tumor-specific somatic variants were detected in cfDNA at the time of diagnosis and 8.4 ± 2.4 months prior to detection of locoregional recurrence or distant metastases. Our results demonstrate that the sequencing of cfDNA may be used for the early detection of locoregional and distant BC metastases. Observed discordance between tumor DNA sequencing and liquid biopsy supports the parallel sequencing of cfDNA and tumor DNA to yield the most comprehensive overview for the genetic landscape of BC.

High Cell-Free DNA Integrity Is Associated with Poor Breast Cancer Survival.

BACKGROUND: A recent point of focus in breast cancer (BC) research has been the utilization of cell-free DNA (cfDNA) and its concentration (cfDConc) and integrity (cfDI) as potential biomarkers. Though the association of cfDConc and poor survival is already recognized, studies on the prognostic value of cfDI have had contradictory results. Here, we provide further evidence to support the use of cfDI as a potential biomarker. METHODS: We selected 204 Eastern Finnish BC cases with non-metastatic disease and isolated cfDNA from the serum collected at the time of diagnosis before any treatment was given. The cfDConc and cfDI were measured with a fluorometer and electrophoresis and analyzed with 25 years of survival data. RESULTS: High cfDConc was not an independent prognostic factor in our analyses while high cfDI was found to be an independent prognostic factor for poor OS (p = 0.020, hazard ratio (HR) = 1.57, 95% confidence interval (CI) 1.07-2.29, Cox) and BCSS (p = 0.006, HR = 1.93, 95% CI 1.21-3.08)). Inclusion of cfDI in the multivariate logistic regression model improved the predictive performance. CONCLUSIONS: Our results show high cfDI is an independent prognostic factor for poor OS and BCSS and improves the predictive performance of logistic regression models, thus supporting its prognostic potential.

Multi-level dilated residual network for biomedical image segmentation.

We propose a novel multi-level dilated residual neural network, an extension of the classical U-Net architecture, for biomedical image segmentation. U-Net is the most popular deep neural architecture for biomedical image segmentation, however, despite being state-of-the-art, the model has a few limitations. In this study, we suggest replacing convolutional blocks of the classical U-Net with multi-level dilated residual blocks, resulting in enhanced learning capability. We also propose to incorporate a non-linear multi-level residual blocks into skip connections to reduce the semantic gap and to restore the information lost when concatenating features from encoder to decoder units. We evaluate the proposed approach on five publicly available biomedical datasets with different imaging modalities, including electron microscopy, magnetic resonance imaging, histopathology, and dermoscopy, each with its own segmentation challenges. The proposed approach consistently outperforms the classical U-Net by 2%, 3%, 6%, 8%, and 14% relative improvements in dice coefficient, respectively for magnetic resonance imaging, dermoscopy, histopathology, cell nuclei microscopy, and electron microscopy modalities. The visual assessments of the segmentation results further show that the proposed approach is robust against outliers and preserves better continuity in boundaries compared to the classical U-Net and its variant, MultiResUNet.

The debatable presence of PIWI-interacting RNAs in invasive breast cancer.

Numerous factors influence breast cancer (BC) prognosis, thus complicating the prediction of outcome. By identifying biomarkers that would distinguish the cases with poorer response to therapy already at the time of diagnosis, the rate of survival could be improved. Lately, Piwi-interacting RNAs (piRNAs) have been introduced as potential cancer biomarkers, however, due to the recently raised challenges in piRNA annotations, further evaluation of piRNAs' involvement in cancer is required. We performed small RNA sequencing in 227 fresh-frozen breast tissue samples from the Eastern Finnish Kuopio Breast Cancer Project material to study the presence of piRNAs in BC and their associations with the clinicopathological features and outcome of BC patients. We observed the presence of three small RNAs annotated as piRNA database entries (DQ596932, DQ570994, and DQ571955) in our samples. The actual species of these RNAs however remain uncertain. All three small RNAs were upregulated in grade III tumors and DQ596932 additionally in estrogen receptor negative tumors. Furthermore, patients with estrogen receptor positive BC and higher DQ571955 had shorter relapse-free survival and poorer BC-specific survival, thus indicating DQ571955 as a candidate predictive marker for radiotherapy response in estrogen receptor positive BC. DQ596932 showed possible prognostic value in BC, whereas DQ570994 was identified as a candidate predictive marker for tamoxifen and chemotherapy response. These three small RNAs appear as candidate biomarkers for BC, which could after further investigation provide novel approaches for the treatment of therapy resistant BC. Overall, our results indicate that the prevalence of piRNAs in cancer is most likely not as comprehensive as has been previously thought.

Microvessels in Epithelial Ovarian Tumors: High Microvessel Density Is a Significant Feature of Malignant Ovarian Tumors.

BACKGROUND/AIM: Examine features of blood and lymphatic vessels in ovarian tumors and their significance to prognosis of ovarian cancer. PATIENTS AND METHODS: A total of 139 women with epithelial ovarian tumors were included: 86 malignant, 17 borderline and 36 benign. Density, percentage, mean size and number of blood microvessels in tumors were measured by immunohistochemistry with antibodies against CD34 and CD105. Lymphatic vessel density was assayed using the D2-40 antibody against podoplanin. RESULTS: Angiogenesis was most profuse in malignant tumors. Small size of lymph vessels predicted 26% shorter 5-year survival of ovarian cancer patients. Further, high percentage of lymphatic vessels in tumors was associated with lymph node metastasis, and high density with cancer recurrence. Lower number of microvessels, as assessed by CD34 staining, predicted shorter progression-free survival. Additionally, the large size of microvessels assessed by CD34 and the high number of vessels assessed by CD105 were related to residual tumor >1 cm at primary surgery and also, large vessel size was associated with stage III, as assessed by CD105 staining. CONCLUSION: CD34 and CD105 define different characteristics of microvessels. Parameters of lymph vessels may predict the prognosis of ovarian cancer.

High expression of Tie-2 predicts poor prognosis in primary high grade serous ovarian cancer.

BACKGROUND: Antiangiogenic therapy, although part of standard treatment in ovarian cancer, has variable efficacy. Furthermore, little is known about the prognostic biomarkers and factors influencing angiogenesis in cancer tissue. We evaluated the expression of angiopoietin-2 and two endothelial tyrosine kinase receptors, Tie-1 and Tie-2, and assessed their value in the prediction of survival in patients with malignant epithelial ovarian cancer. We also compared the expression of these factors between primary high grade serous tumors and their distant metastasis. MATERIALS AND METHODS: We evaluated 86 women with primary epithelial ovarian cancer. Matched distal omental metastasis were investigated in 18.6% cases (N = 16). The expression levels of angiogenic factors were evaluated by immunohistochemistry in 306 specimens and by qRT-PCR in 111 samples. RESULTS: A high epithelial expression level of Tie-2 is a significant prognostic factor in primary high grade serous ovarian cancer. It predicted significantly shorter overall survival both in univariate (p<0.001) and multivariate survival analyses (p = 0.022). Low angiopoietin-2 expression levels in primary ovarian tumors were significantly associated with shorter overall survival (p = 0.015) in the univariate survival analysis. A low expression of angiopoietin-2 was also significantly related to high grade tumors, size of residual tumor after primary surgery and the recurrence of cancer (p = 0.008; p = 0.012; p = 0.018) in the whole study population. The expression of angiopoietin-2 and Tie-2 was stronger in distal omental metastasis than in primary high grade serous tumors in matched-pair analysis (p = 0.001; p = 0.002). CONCLUSIONS: The angiogenic factor, angiopoietin-2, and its receptor Tie-2 seem to be significant prognostic factors in primary epithelial ovarian cancer. Their expression levels are also increased in metastatic lesions in comparison with primary tumors.

Predicting breast cancer risk using interacting genetic and demographic factors and machine learning.

Breast cancer (BC) is a multifactorial disease and the most common cancer in women worldwide. We describe a machine learning approach to identify a combination of interacting genetic variants (SNPs) and demographic risk factors for BC, especially factors related to both familial history (Group 1) and oestrogen metabolism (Group 2), for predicting BC risk. This approach identifies the best combinations of interacting genetic and demographic risk factors that yield the highest BC risk prediction accuracy. In tests on the Kuopio Breast Cancer Project (KBCP) dataset, our approach achieves a mean average precision (mAP) of 77.78 in predicting BC risk by using interacting genetic and Group 1 features, which is better than the mAPs of 74.19 and 73.65 achieved using only Group 1 features and interacting SNPs, respectively. Similarly, using interacting genetic and Group 2 features yields a mAP of 78.00, which outperforms the system based on only Group 2 features, which has a mAP of 72.57. Furthermore, the gene interaction maps built from genes associated with SNPs that interact with demographic risk factors indicate important BC-related biological entities, such as angiogenesis, apoptosis and oestrogen-related networks. The results also show that demographic risk factors are individually more important than genetic variants in predicting BC risk.

High mutation burden of circulating cell-free DNA in early-stage breast cancer patients is associated with a poor relapse-free survival.

BACKGROUND: High tumor mutation burden is shown to be associated with a poor clinical outcome. As the tumor-derived fraction of circulating cell-free DNA (cfDNA) is shown to reflect the genetic spectrum of the tumor, we examined whether the mutation burden of cfDNA could be used to predict the clinical outcomes of early-stage breast cancer (BC) patients. METHODS: We selected a set of 79 Finnish early-stage BC cases with a good prognosis based on traditional prognostic parameters but some of which still developed relapsed disease during follow-up. cfDNA was isolated from the serum collected at the time of diagnosis, sequenced, and compared to matched primary tumors, clinical parameters, and survival data. RESULTS: High cfDNA mutation burden was associated with the poor relapse-free survival (RFS) (P = .016, HR = 2.23, 95% Cl 1.16-4.27) when patients were divided into high and low mutation burden according to the median number of somatic variants. A high discordance was observed between the matched tumor and cfDNA samples, thus highlighting the challenges related to the liquid biopsy of early-stage cancer cases. Despite the low number of detected tumor-specific variants, the presence of tumor-specific somatic variants in the cfDNA was associated with the poor RFS (P = .009, HR = 2.31, 95% Cl 1.23-4.31). CONCLUSIONS: Our results confirm previously observed challenges about the accuracy of liquid biopsy-based genotyping of early-stage cancers and support the parallel sequencing of tumor and cfDNA while also demonstrating how the presence of tumor-specific somatic variants and the high mutation burden in the cfDNA are both associated with the poor RFS, thus indicating the prognostic potential of liquid biopsy in the context of early-stage cancers.

MicroRNAs Associated With Biological Pathways of Left- and Right-sided Colorectal Cancer.

BACKGROUND/AIM: MicroRNAs (miRNAs) regulate the development of colorectal cancer (CRC). We aimed to investigate miRNAs and their relation to cancer-related signaling pathways in site-specific CRC. MATERIALS AND METHODS: We used a total of 24 left- and right-sided Finnish CRC samples (discovery cohort) and The Cancer Genome Atlas public mature miRSeq dataset of 201 CRC samples (validation cohort). MiRNA differential expression and biological pathway analyses were performed using DESeq2 and the DIANA/mirPath tool, respectively. RESULTS: We found 17 significantly differentially up-regulated [false discovery rate (FDR) <0.05] miRNAs in left-sided CRC ("left miRNAs"), and 15 in right-sided CRC ("right miRNAs"). The left miRNAs participate in the mTor, Wnt, PI3K-Akt signaling pathways (FDR<0.05). The right miRNAs participate in the TGF-ß signaling pathway. We also observed that both cohorts share six miRNAs. One of these (hsa-miR-196b-5p) was significantly (FDR<0.05) up-regulated in left-sided CRC. The rest of them (hsa-miR-625-3p, hsa-miR-155-5p, hsa-miR-625-5p, hsa-miR-31-5p and hsa-miR-330-5p) showed significant (FDR<0.05) up-regulation in right-sided CRC. CONCLUSION: Left and right miRNAs are associated with predominant biological pathways of left- and right-sided CRC, respectively. Our results may be beneficial for classifying CRC and for future biomarker studies of site-specific CRC.

Peritumor to tumor apparent diffusion coefficient ratio is associated with biologically more aggressive breast cancer features and correlates with the prognostication tools.

PURPOSE: The apparent diffusion coefficient (ADC) is increasingly used to characterize breast cancer. The peritumor/tumor ADC ratio is suggested to be a reliable and generally applicable index. However, its overall prognostication value remains unclear. We aimed to evaluate the associations between the peritumor/tumor ADC ratio and histopathological biomarkers and published prognostic tools in patients with invasive breast cancer. MATERIALS AND METHODS: This prospective study included 88 lesions (five bilateral) in 83 patients with primary invasive breast cancer who underwent preoperative 3.0-T magnetic resonance imaging. The lowest intratumoral mean ADC value on the slice with the largest tumor cross-sectional area was designated the tumor ADC, and the highest mean ADC value on the peritumoral breast parenchymal tissue adjacent to the tumor border was designated the peritumor ADC. The peritumor/tumor ADC ratio was then calculated. The tumor and peritumor ADC values and peritumor/tumor ADC ratios were compared with histopathological parameters using an unpaired t test, and their correlations with published prognostic tools were evaluated with Pearson's correlation coefficient. RESULTS: The peritumor/tumor ADC ratio was significantly associated with tumor size (p<0.001), histological grade (p = 0.005), Ki-67 index (p = 0.006), axillary-lymph-node metastasis (p = 0.001), and lymphovascular invasion (p = 0.006), but was not associated with estrogen receptor status (p = 0.931), progesterone receptor status (p = 0.160), or human epidermal growth factor receptor 2 status (p = 0.259). The peritumor/tumor ADC ratio showed moderate positive correlations with the Nottingham Prognostic Index (r = 0.498, p<0.001) and mortality predicted using PREDICT (r = 0.436, p<0.001). CONCLUSION: The peritumor/tumor ADC ratio was correlated with histopathological biomarkers in patients with invasive breast cancer, showed significant correlations with published prognostic indexes, and may provide an easily applicable imaging index for the preoperative prognostic evaluation of breast cancer.

Expression profiles of VEGF-A, VEGF-D and VEGFR1 are higher in distant metastases than in matched primary high grade epithelial ovarian cancer.

BACKGROUND: In many malignancies including ovarian cancer, different angiogenic factors have been related to poor prognosis. However, data on their relations to each other or importance as a prognostic factor in ovarian cancer is missing. Therefore, we investigated the expressions of VEGF-A, VEGF-C, and VEGF-D, and the receptors VEGFR1, VEGFR2, and VEGFR3 in patients with malignant epithelial ovarian neoplasms. We further compared expression levels between primary tumors and related distant omental metastases. METHODS: This study included 86 patients with malignant ovarian epithelial tumors and 16 related distant metastases. Angiogenic factor expression was evaluated using immunohistochemistry (n = 102) and qRT-PCR (n = 29). RESULTS: Compared to primary high grade serous ovarian tumors, the related omental metastases showed higher expressions of VEGF-A (p = 0.022), VEGF-D (p = 0.010), and VEGFR1 (p = 0.046). In univariate survival analysis, low epithelial expression of VEGF-A in primary tumors was associated with poor prognosis (p = 0.024), and short progression-free survival was associated with high VEGF-C (p = 0.034) and low VEGFR3 (p = 0.002). The relative expressions of VEGF-D, VEGFR1, VEGFR2, and VEGFR3 mRNA determined by qRT-PCR analyses were significantly correlated with the immunohistochemically detected levels of these proteins in primary high grade serous ovarian cancer and metastases (p = 0.004, p = 0.009, p = 0.015, and p = 0.018, respectively). CONCLUSIONS: The expressions of VEGF receptors and their ligands significantly differed between malignant ovarian tumors and paired distant metastases. VEGF-A, VEGF-D, and VEGFR1 protein expressions seem to be higher in distant metastases than in the primary high grade serous ovarian cancer lesions.

Machine learning identifies interacting genetic variants contributing to breast cancer risk: A case study in Finnish cases and controls.

We propose an effective machine learning approach to identify group of interacting single nucleotide polymorphisms (SNPs), which contribute most to the breast cancer (BC) risk by assuming dependencies among BCAC iCOGS SNPs. We adopt a gradient tree boosting method followed by an adaptive iterative SNP search to capture complex non-linear SNP-SNP interactions and consequently, obtain group of interacting SNPs with high BC risk-predictive potential. We also propose a support vector machine formed by the identified SNPs to classify BC cases and controls. Our approach achieves mean average precision (mAP) of 72.66, 67.24 and 69.25 in discriminating BC cases and controls in KBCP, OBCS and merged KBCP-OBCS sample sets, respectively. These results are better than the mAP of 70.08, 63.61 and 66.41 obtained by using a polygenic risk score model derived from 51 known BC-associated SNPs, respectively, in KBCP, OBCS and merged KBCP-OBCS sample sets. BC subtype analysis further reveals that the 200 identified KBCP SNPs from the proposed method performs favorably in classifying estrogen receptor positive (ER+) and negative (ER-) BC cases both in KBCP and OBCS data. Further, a biological analysis of the identified SNPs reveals genes related to important BC-related mechanisms, estrogen metabolism and apoptosis.

Tumor-infiltrating lymphocytes associate with outcome in nonendemic nasopharyngeal carcinoma: a multicenter study.

The prognostic significance of tumor-infiltrating lymphocytes (TILs) has been studied recently in many cancers. For the first time in a nonendemic region, we have evaluated the prognostic value of TILs in a whole population-based nationwide cohort of nasopharyngeal carcinoma (NPC) in Finland. A total of 115 cases from Finnish hospitals were included. TILs were analyzed using hematoxylin and eosin-stained slides according to the criteria of the International Immuno-Oncology Biomarker Working Group. TILs were evaluated separately in stromal and tumor compartments. The log-rank test and univariable and multivariable analyses were used to compare survival in patients with tumors with low and high TILs. A significant positive correlation was observed between the occurrence of intratumoral and stromal TILs (P < .001). In multivariable analysis, NPC cases with low intratumoral TILs had poor overall survival with a hazard ratio (HR) of 2.55 and 95% confidence interval (95% CI) of 1.60 to 4.05 (P < .001). Cases with low intratumoral TILs also had poor disease-specific survival (HR, 2.02; 95% CI, 1.16-3.52; P = .015). Keratinized tumors with low intratumoral TILs were associated with an even poorer overall survival (HR, 3.94; 95% CI, 2.17-7.15; P < .001) and a poor disease-specific survival (HR, 2.97; 95% CI, 1.46-6.05; P = .009). Our study demonstrates that the evaluation of TILs is simple and can be assessed routinely in NPC.