High Numbers of CD163+ Tumor-Associated Macrophages Predict Poor Prognosis in HER2+ Breast Cancer.

Tumor-associated macrophages (TAMs) are associated with a poor outcome in breast cancer (BC), but their prognostic value in different BC subtypes has remained somewhat unclear. Here, we investigated the prognostic value of M2-like TAMs (CD163+) and all TAMs (CD68+) in a patient cohort of 278 non-metastatic BC patients, half of whom were HER2+ (n = 139). The survival endpoints investigated were overall survival (OS), breast cancer-specific survival (BCSS) and disease-free survival (DFS). In the whole patient cohort (n = 278), a high CD163+ TAM count and a high CD68+ TAM count were associated with a worse outcome (p ≤ 0.023). In HER2+ BC, a high CD163+ TAM count was an independent factor for a poor prognosis across all the investigated survival endpoints (p < 0.001). The prognostic effect was evident in both the HER2+/hormone receptor-positive (p < 0.001) and HER2+/hormone receptor-negative (p ≤ 0.012) subgroups and regardless of the provision of adjuvant trastuzumab (p ≤ 0.002). In HER2-negative BC, the CD163+ TAM count was not significantly associated with survival. These results suggest that a high CD163+ TAM count predicts an inferior outcome, especially in HER2+ BC patients, and as adjuvant trastuzumab did not overcome the poor prognostic effect, combination treatments including therapies targeting the macrophage function could represent an effective therapeutic approach in HER2+ BC.

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.

Spectrum and Frequency of Germline FANCM Protein-Truncating Variants in 44,803 European Female Breast Cancer Cases.

FANCM germline protein truncating variants (PTVs) are moderate-risk factors for ER-negative breast cancer. We previously described the spectrum of FANCM PTVs in 114 European breast cancer cases. In the present, larger cohort, we report the spectrum and frequency of four common and 62 rare FANCM PTVs found in 274 carriers detected among 44,803 breast cancer cases. We confirmed that p.Gln1701* was the most common PTV in Northern Europe with lower frequencies in Southern Europe. In contrast, p.Gly1906Alafs*12 was the most common PTV in Southern Europe with decreasing frequencies in Central and Northern Europe. We verified that p.Arg658* was prevalent in Central Europe and had highest frequencies in Eastern Europe. We also confirmed that the fourth most common PTV, p.Gln498Thrfs*7, might be a founder variant from Lithuania. Based on the frequency distribution of the carriers of rare PTVs, we showed that the FANCM PTVs spectra in Southwestern and Central Europe were much more heterogeneous than those from Northeastern Europe. These findings will inform the development of more efficient FANCM genetic testing strategies for breast cancer cases from specific European populations.

The prognostic and predictive role of tumor-infiltrating lymphocytes (FoxP3 + and CD8 +) and tumor-associated macrophages in early HER2 + breast cancer.

PURPOSE: In HER2-positive (HER2 +) breast cancer, tumor-infiltrating lymphocytes (TILs) and tumor-associated macrophages (TAMs) may influence the efficacy of the HER2-antibody trastuzumab and the patient's outcome. In this HER2 + patient cohort, our aim was to study the numbers of FoxP3 + regulatory TILs and CD8 + cytotoxic TILs, their correlations with CD68 + and CD163 + TAMs, and the prognostic and predictive value of the studied factors. METHODS: We evaluated 139 non-metastatic HER2 + breast cancer patients operated between 2001 and 2008. The FoxP3+TIL count (FoxP3+TILs) was assessed using the hotspot method, and the CD8 + TIL count (CD8+mTILs) utilizing a digital image analysis from invasive margin areas. The ratios between CD8+mTILs and FoxP3+TILs as well as CD8+mTILs and TAMs were calculated. RESULTS: FoxP3 + TILs and CD8 + mTILs correlated positively with each other (p<0.001). FoxP3+TILs had a positive correlation with CD68+and CD163+TAMs (p≤0.038), while CD8 + mTILs correlated only with CD68+TAMs (p<0.001). In the HER2 + and hormone receptor-positive Luminal B subgroup, high numbers of FoxP3+TILs were associated with shorter disease-free survival (DFS) (54% vs. 79%, p = 0.040). The benefit from adjuvant trastuzumab was extremely significant among patients with a high CD8 + mTILs/CD68 + TAMs ratio, with overall survival (OS) 84% vs. 33% (p = 0.003) and breast cancer-specific survival (BCSS) 88% vs. 48% (p = 0.009) among patients treated with or without trastuzumab, respectively. CONCLUSION: In the HER2 + Luminal B subgroup, high FoxP3 + TILs were associated with shorter DFS. A high CD8 + mTILs/CD68 + TAMs ratio seems to associate with impressive efficacy of trastuzumab.

PREDICT validity for prognosis of breast cancer patients with pathogenic BRCA1/2 variants.

We assessed the PREDICT v 2.2 for prognosis of breast cancer patients with pathogenic germline BRCA1 and BRCA2 variants, using follow-up data from 5453 BRCA1/2 carriers from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) and the Breast Cancer Association Consortium (BCAC). PREDICT for estrogen receptor (ER)-negative breast cancer had modest discrimination for BRCA1 carrier patients overall (Gönen & Heller unbiased concordance 0.65 in CIMBA, 0.64 in BCAC), but it distinguished clearly the high-mortality group from lower risk categories. In an analysis of low to high risk categories by PREDICT score percentiles, the observed mortality was consistently lower than the expected mortality, but the confidence intervals always included the calibration slope. Altogether, our results encourage the use of the PREDICT ER-negative model in management of breast cancer patients with germline BRCA1 variants. For the PREDICT ER-positive model, the discrimination was slightly lower in BRCA2 variant carriers (concordance 0.60 in CIMBA, 0.65 in BCAC). Especially, inclusion of the tumor grade distorted the prognostic estimates. The breast cancer mortality of BRCA2 carriers was underestimated at the low end of the PREDICT score distribution, whereas at the high end, the mortality was overestimated. These data suggest that BRCA2 status should also be taken into consideration with tumor characteristics, when estimating the prognosis of ER-positive breast cancer patients.

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.

An assessment of prevalence of Type 1 CFI rare variants in European AMD, and why lack of broader genetic data hinders development of new treatments and healthcare access.

PURPOSE: Advanced age-related macular degeneration (AAMD) risk is associated with rare complement Factor I (FI) genetic variants associated with low FI protein levels (termed 'Type 1'), but it is unclear how variant prevalences differ between AMD patients from different ethnicities. METHODS: Collective prevalence of Type 1 CFI rare variant genotypes were examined in four European AAMD datasets. Collective minor allele frequencies (MAFs) were sourced from the natural history study SCOPE, the UK Biobank, the International AMD Genomics Consortium (IAMDGC), and the Finnish Biobank Cooperative (FINBB), and compared to paired control MAFs or background population prevalence rates from the Genome Aggregation Database (gnomAD). Due to a lack of available genetic data in non-European AAMD, power calculations were undertaken to estimate the AAMD population sizes required to identify statistically significant association between Type 1 CFI rare variants and disease risk in different ethnicities, using gnomAD populations as controls. RESULTS: Type 1 CFI rare variants were enriched in all European AAMD cohorts, with odds ratios (ORs) ranging between 3.1 and 7.8, and a greater enrichment was observed in dry AMD from FINBB (OR 8.9, 95% CI 1.49-53.31). The lack of available non-European AAMD datasets prevented us exploring this relationship more globally, however a statistical association may be detectable by future sequencing studies that sample approximately 2,000 AAMD individuals from Ashkenazi Jewish and Latino/Admixed American ethnicities. CONCLUSIONS: The relationship between Type 1 CFI rare variants increasing odds of AAMD are well established in Europeans, however the lack of broader genetic data in AAMD has adverse implications for clinical development and future commercialisation strategies of targeted FI therapies in AAMD. These findings emphasise the importance of generating more diverse genetic data in AAMD to improve equity of access to new treatments and address the bias in health care.

Malignant and in situ subtypes of melanoma are associated with basal and squamous cell carcinoma and its precancerous lesions

Background: Patients with cutaneous malignant melanoma (CMM) are at increased risk of non-melanoma skin cancers (NMSCs) and possibly precancerous lesions. Objectives: To analyse the association between CMM and not only NMSCs but also precursor lesions, actinic keratosis (AK) and Bowen disease (BD). Materials & Methods: The Finnish Cancer Registry data was used to calculate the age-standardized incidence ratio during 2000-2013 for basal (BCC) and squamous (SCC) cell carcinoma in patients with CMM. All tissue material collected from 70,420 subjects during 2000-2013 and reposited in the Biobank of Eastern Finland was used to calculate the age-standardized prevalence of BCC, SCC, BD and AK in CMM patients. Results: In both genders, the age-standardized incidence ratio of BCC and SCC was increased in CMM patients. The age-standardized prevalence of NMSCs and precursor lesions was higher in patients with CMM than in those without CMM, and was higher in CMM patients with immunosuppression (IS) than in those without IS. The association of M-Snomed subtypes, lentigo maligna (LM), melanoma in situ (MIS) and malignant melanoma (MM) with AK and/or BD was stronger than with BCC. LM revealed the highest association with the combination of AKBD-SCC. Male subjects showed a higher age-standardized prevalence of CMM, MM and BCC than females, but the opposite was observed for AK. Conclusion: Melanoma increases the risk of NMSCs, and IS may enhance this risk. Both malignant and in situ subtypes of melanoma associate with not only BCC and SCC, but also precancerous lesions.

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 regional mortality due to malignant melanoma in Eastern Finland may be explained by the increase in aggressive melanoma types.

BACKGROUND: A regional skin cancer prevention program in Eastern Finland revealed a relatively high age-standardized mortality due to malignant melanoma during 2013-2017. An explanation for this is needed. PURPOSE: To analyse the 543 melanoma samples in 524 subjects collected during 2000-2013 at Kuopio University Hospital and reposited in the Biobank of Eastern Finland. A focus was directed to factors related to metastasis. METHODS: The samples were analysed anonymously by examining the histopathological report, referral text and the list of diagnoses. A possible state of immunosuppression was evaluated. RESULTS: The mean age at the diagnosis of malignant melanoma (MM), lentigo maligna (LM) and melanoma in situ was relatively high, i.e., 66.2, 72.1 and 63.3, respectively. Especially the MM type increased markedly during 2000-2013. In further analyses of a representative cohort of 337 samples, the proportion of nodular melanoma and LM/LMM melanoma was relatively high, 35.6 and 22.0%, respectively, but that from superficial spreading melanoma relatively low (33.8%). Metastasis correlated with immunosuppression, male gender, Clark level, Breslow thickness, ulceration, mitosis count, invasion into vessels and/or perineural area, microsatellites, melanoma subtype, body site, recidivism, and the absence of dysplastic nevus cells. CONCLUSION: The marked increase in aggressive melanomas with associated metastasis, and the relatively high age at diagnosis, can partially explain the mortality.

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.

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.

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.

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.