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Item does not contain fulltext, BACKGROUND: Invasive breast cancer patients are increasingly being treated with neoadjuvant chemotherapy; however, only a fraction of the patients respond to it completely. To prevent overtreatment, there is an urgent need for biomarkers to predict treatment response before administering the therapy. METHODS: In this retrospective study, we developed hypothesis-driven interpretable biomarkers based on deep learning, to predict the pathological complete response (pCR, i.e., the absence of tumor cells in the surgical resection specimens) to neoadjuvant chemotherapy solely using digital pathology H&E images of pre-treatment breast biopsies. Our approach consists of two steps: First, we use deep learning to characterize aspects of the tumor micro-environment by detecting mitoses and segmenting tissue into several morphology compartments including tumor, lymphocytes and stroma. Second, we derive computational biomarkers from the segmentation and detection output to encode slide-level relationships of components of the tumor microenvironment, such as tumor and mitoses, stroma, and tumor infiltrating lymphocytes (TILs). RESULTS: We developed and evaluated our method on slides from n = 721 patients from three European medical centers with triple-negative and Luminal B breast cancers and performed external independent validation on n = 126 patients from a public dataset. We report the predictive value of the investigated biomarkers for predicting pCR with areas under the receiver operating characteristic curve between 0.66 and 0.88 across the tested cohorts. CONCLUSION: The proposed computational biomarkers predict pCR, but will require more evaluation and finetuning for clinical application. Our results further corroborate the potential role of deep learning to automate TILs quantification, and their predictive value in breast cancer neoadjuvant treatment planning, along with automated mitoses quantification. We made our method publicly available to extract segmentation-bas

Contains fulltext : 238668.pdf (Publisher’s version ) (Open Access), PURPOSE: The presence of extensive ductal carcinoma in situ (DCIS) adjacent to HER2-positive invasive breast cancer (IBC) is often a contra-indication for breast-conserving surgery, even in case of excellent treatment response of the invasive component. Data on the response of DCIS to neoadjuvant systemic treatment (NST) are limited. Therefore, we estimated the response of adjacent DCIS to NST-containing HER2-blockade in HER2-positive breast cancer patients and assessed the association of clinicopathological and radiological factors with response. METHODS: Pre-NST biopsies were examined to determine presence of DCIS in all women with HER2-positive IBC treated with trastuzumab-containing NST +/- pertuzumab between 2004 and 2017 in a comprehensive cancer center. When present, multiple DCIS factors, including grade, calcifications, necrosis, hormone receptor, and Ki-67 expression, were scored. Associations of clinicopathological and radiological factors with complete response were assessed using logistic regression models. RESULTS: Adjacent DCIS, observed in 138/316 patients with HER2-positive IBC, was eradicated after NST in 46% of patients. Absence of calcifications suspicious for malignancy on pre-NST mammography (odds ratio (OR) 3.75; 95% confidence interval (95% CI) 1.72-8.17), treatment with dual HER2-blockade (OR 2.36; 95% CI 1.17-4.75), a (near) complete response on MRI (OR 3.55; 95% CI 1.31-9.64), and absence of calcifications (OR 3.19; 95% CI 1.34-7.60) and Ki-67 > 20% in DCIS (OR 2.74; 95% CI 1.09-6.89) on pre-NST biopsy were significantly associated with DCIS response. CONCLUSIONS: As DCIS can respond to NST containing HER2-blockade, the presence of extensive DCIS in HER2-positive breast cancer before NST should not always indicate a mastectomy. The predictive factors we found could be helpful when considering breast-conserving surgery in these patients.

We aimed to assess contralateral breast cancer (CBC) risk in patients with ductal carcinoma in situ (DCIS) compared with invasive breast cancer (BC). Women diagnosed with DCIS (N = 28,003) or stage I–III BC (N = 275,836) between 1989 and 2017 were identified from the nationwide Netherlands Cancer Registry. Cumulative incidences were estimated, accounting for competing risks, and hazard ratios (HRs) for metachronous invasive CBC. To evaluate effects of adjuvant systemic therapy and screening, separate analyses were performed for stage I BC without adjuvant systemic therapy and by mode of first BC detection. Multivariable models including clinico-pathological and treatment data were created to assess CBC risk prediction performance in DCIS patients. The 10- year cumulative incidence of invasive CBC was 4.8% for DCIS patients (CBC = 1334). Invasive CBC risk was higher in DCIS patients compared with invasive BC overall (HR = 1.10, 95% confidence interval (CI) = 1.04–1.17), and lower compared with stage I BC without adjuvant systemic therapy (HR = 0.87; 95% CI = 0.82–0.92). In patients diagnosed ≥2011, the HR for invasive CBC was 1.38 (95% CI = 1.35–1.68) after screen-detected DCIS compared with screen-detected invasive BC, and was 2.14 (95% CI = 1.46–3.13) when not screen-detected. The C-index was 0.52 (95% CI = 0.50–0.54) for invasive CBC prediction in DCIS patients. In conclusion, CBC risks are low overall. DCIS patients had a slightly higher risk of invasive CBC compared with invasive BC, likely explained by the risk-reducing effect of (neo)adjuvant systemic therapy among BC patients. For support of clinical decision making more information is needed to differentiate CBC risks among DCIS patients

Intratumour heterogeneity fuels carcinogenesis and allows circumventing specific targeted therapies. HER2 gene amplification is associated with poor outcome in invasive breast cancer. Heterogeneous HER2 amplification has been described in 5–41% of breast cancers. Here, we investigated the genetic differences between HER2-positive and HER2-negative admixed breast cancer components. We performed an in-depth analysis to explore the potential heterogeneity in the somatic mutational landscape of each individual tumour component. Formalin-fixed, paraffin-embedded breast cancer tissue of ten patients with at least one HER2-negative and at least one HER2-positive component was microdissected. Targeted next-generation sequencing was performed using a customized 53-gene panel. Somatic mutations and copy number variations were analysed. Overall, the tumours showed a heterogeneous distribution of 12 deletions, 9 insertions, 32 missense variants and 7 nonsense variants in 26 different genes, which are (likely) pathogenic. Three splice site alterations were identified. One patient had an EGFR copy number gain restricted to a HER2-negative in situ component, resulting in EGFR protein overexpression. Two patients had FGFR1 copy number gains in at least one tumour component. Two patients had an 8q24 gain in at least one tumour component, resulting in a copy number increase in MYC and PVT1. One patient had a CCND1 copy number gain restricted to a HER2- negative tumour compo

Purpose: Gene expression (GE) profiling for breast cancer classification and prognostication has become increasingly used in clinical diagnostics. GE profiling requires a reasonable tumor cell percentage and high-quality RNA. As a consequence, a certain amount of samples drop out. If tumor characteristics are different between samples included and excluded from GE profiling, this can lead to bias. Therefore, we assessed whether patient and tumor characteristics differ between tumors suitable or unsuitable for generating GE profiles in breast cancer. Methods: In a consecutive cohort of 738 breast cancer patients who received neoadjuvant chemotherapy at the Netherlands Cancer Institute, GE profiling was performed. We compared tumor characteristics and treatment outcome between patients included and excluded from GE profiling. Results were validated in an independent cohort of 812 patients treated with primary surgery. Results: GE analysis could be performed in 53% of the samples. Patients with tumor GE profiles more often had high-grade tumors [odds ratio 2.57 (95%CI 1.77–3.72), p < 0.001] and were more often lymph node positive [odds ratio 1.50 (95%CI 1.03–2.19), p = 0.035] compared to the group for which GE profiling was not possible. In the validation cohort, tumors suitable for gene expression analysis were more often high grade. Conclusions: In our gene expression studies, tumors suitable for GE profiling had more often an unfavorable prognostic profile. Due to selection of samples with a high tumor percentage, we automatically select for tumors with specific features, i.e., tumors with a higher grade and lymph node involvement. It is important to be aware of this phenomenon when performing gene expression analysis in a research or clinical context.

Contains fulltext : 153904.pdf (publisher's version ) (Open Access), Breast cancers with BRCA1 germline mutation have a characteristic DNA copy number (CN) pattern. We developed a test that assigns CN profiles to be 'BRCA1-like' or 'non-BRCA1-like', which refers to resembling a BRCA1-mutated tumor or resembling a tumor without a BRCA1 mutation, respectively. Approximately one third of the BRCA1-like breast cancers have a BRCA1 mutation, one third has hypermethylation of the BRCA1 promoter and one third has an unknown reason for being BRCA1-like. This classification is indicative of patients' response to high dose alkylating and platinum containing chemotherapy regimens, which targets the inability of BRCA1 deficient cells to repair DNA double strand breaks. We investigated whether this classification can be reliably obtained with next generation sequencing and copy number platforms other than the bacterial artificial chromosome (BAC) array Comparative Genomic Hybridization (aCGH) on which it was originally developed. We investigated samples from 230 breast cancer patients for which a CN profile had been generated on two to five platforms, comprising low coverage CN sequencing, CN extraction from targeted sequencing panels (CopywriteR), Affymetrix SNP6.0, 135K/720K oligonucleotide aCGH, Affymetrix Oncoscan FFPE (MIP) technology, 3K BAC and 32K BAC aCGH. Pairwise comparison of genomic position-mapped profiles from the original aCGH platform and other platforms revealed concordance. For most cases, biological differences between samples exceeded the differences between platforms within one sample. We observed the same classification across different platforms in over 80% of the patients and kappa values of at least 0.36. Differential classification could be attributed to CN profiles that were not strongly associated to one class. In conclusion, we have shown that the genomic regions that define our BRCA1-like classifier are robustly measured by different CN profiling technologies, providing the possibility to retro- and prospectively in

Breast cancers with BRCA1 germline mutation have a characteristic DNA copy number (CN) pattern. We developed a test that assigns CN profiles to be ‘BRCA1-like’ or ‘non-BRCA1-like’, which refers to resembling a BRCA1-mutated tumor or resembling a tumor without a BRCA1 mutation, respectively. Approximately one third of the BRCA1-like breast cancers have a BRCA1 mutation, one third has hypermethylation of the BRCA1 promoter and one third has an unknown reason for being BRCA1-like. This classification is indicative of patients' response to high dose alkylating and platinum containing chemotherapy regimens, which targets the inability of BRCA1 deficient cells to repair DNA double strand breaks. We investigated whether this classification can be reliably obtained with next generation sequencing and copy number platforms other than the bacterial artificial chromosome (BAC) array Comparative Genomic Hybridization (aCGH) on which it was originally developed. We investigated samples from 230 breast cancer patients for which a CN profile had been generated on two to five platforms, comprising low coverage CN sequencing, CN extraction from targeted sequencing panels (CopywriteR), Affymetrix SNP6.0, 135K/720K oligonucleotide aCGH, Affymetrix Oncoscan FFPE (MIP) technology, 3K BAC and 32K BAC aCGH. Pairwise comparison of genomic position-mapped profiles from the original aCGH platform and other platforms revealed concordance. For most cases, biological differences between samples exceeded the differences between platforms within one sample. We observed the same classification across different platforms in over 80% of the patients and kappa values of at least 0.36. Differential classification could be attributed to CN profiles that were not strongly associated to one class. In conclusion, we have shown that the genomic regions that define our BRCA1-like classifier are robustly measured by different CN profiling technologies, providing the possibility to retro- and prospectively in, Intelligent Systems, Electrical Engineering, Mathematics and Computer Science

Introduction: Despite continuous efforts, not a single predictor of breast cancer chemotherapy resistance has made it into the clinic yet. However, it has become clear in recent years that breast cancer is a collection of molecularly distinct diseases. With ever increasing amounts of breast cancer data becoming available, we set out to study if gene expression based predictors of chemotherapy resistance that are specific for breast cancer subtypes can improve upon the performance of generic predictors. Methods: We trained predictors of resistance that were specific for a subtype and generic predictors that were not specific for a particular subtype, i.e. trained on all subtypes simultaneously. Through a rigorous double-loop cross-validation we compared the performance of these two types of predictors on the different subtypes on a large set of tumors all profiled on the same expression platform (n = 394). We evaluated predictors based on either mRNA gene expression or clinical features. Results: For HER2+, ER2 breast cancer, subtype specific predictor based on clinical features outperformed the generic, nonspecific predictor. This can be explained by the fact that the generic predictor included HER2 and ER status, features that are predictive over the whole set, but not within this subtype. In all other scenarios the generic predictors outperformed the subtype specific predictors or showed equal performance. Conclusions: Since it depends on the specific context which type of predictor – subtype specific or generic- performed better, it is highly recommended to evaluate both specific and generic predictors when attempting to predict treatment response in breast cancer., Computer Science & Engineering, Electrical Engineering, Mathematics and Computer Science

Introduction: BRCA-mutated breast cancer cells lack the DNA-repair mechanism homologous recombination that is required for error-free DNA double-strand break (DSB) repair. Homologous recombination deficiency (HRD) may cause hypersensitivity to DNA DSB-inducing agents, such as bifunctional alkylating agents and platinum salts. HRD can be caused by BRCA mutations, and by other mechanisms. To identify HRD, studies have focused on triple-negative (TN) breast cancers as these resemble BRCA1-mutated breast cancer closely and might also share this hypersensitivity. However, ways to identify HRD in non-BRCA-mutated, estrogen receptor (ER)-positive breast cancers have remained elusive. The current study provides evidence that genomic patterns resembling BRCA1- or BRCA2-mutated breast cancers can identify breast cancer patients with TN as well as ER-positive, HER2-negative tumors that are sensitive to intensified, DSB-inducing chemotherapy. Methods: Array comparative genomic hybridization (aCGH) was used to classify breast cancers. Patients with tumors with similar aCGH patterns as BRCA1- and/or BRCA2-mutated breast cancers were defined as having a BRCA-likeCGH status, others as non-BCRA-likeCGH. Stage-III patients (n = 249) had participated in a randomized controlled trial of adjuvant high-dose (HD) cyclophosphamide-thiotepa-carboplatin (CTC) versus 5-fluorouracil-epirubicin-cyclophosphamide (FE90C) chemotherapy. Results: Among patients with BRCA-likeCGH tumors (81/249, 32%), a significant benefit of HD-CTC compared to FE90C was observed regarding overall survival (adjusted hazard ratio 0.19, 95% CI: 0.08 to 0.48) that was not seen for patients with non-BRCA-likeCGH tumors (adjusted hazard ratio 0.90, 95% CI: 0.53 to 1.54) (P = 0.004). Half of all BRCA-likeCGH tumors were ER-positive. Conclusions: Distinct aCGH patterns differentiated between HER2-negative patients with a markedly improved outcome after adjuvant treatment with an intensified DNA-DSB-inducing regimen (BRCA-li, Intelligent Systems, Electrical Engineering, Mathematics and Computer Science

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Smoking influences body weight such that smokers weigh less than non-smokers and smoking cessation often leads to weight increase. The relationship between body weight and smoking is partly explained by the effect of nicotine on appetite and metabolism. However, the brain reward system is involved in the control of the intake of both food and tobacco. We evaluated the effect of single-nucleotide polymorphisms (SNPs) affecting body mass index (BMI) on smoking behavior, and tested the 32 SNPs identified in a meta-analysis for association with two smoking phenotypes, smoking initiation (SI) and the number of cigarettes smoked per day (CPD) in an Icelandic sample (N=34 216 smokers). Combined according to their effect on BMI, the SNPs correlate with both SI (r=0.019, P=0.00054) and CPD (r=0.032, P=8.0 × 10-7). These findings replicate in a second large data set (N=127 274, thereof 76 242 smokers) for both SI (P=1.2 × 10-5) and CPD (P=9.3 × 10-5). Notably, the variant most strongly associated with BMI (rs1558902-A in FTO) did not associate with smoking behavior. The association with smoking behavior is not due to the effect of the SNPs on BMI. Our results strongly point to a common biological basis of the regulation of our appetite for tobacco and food, and thus the vulnerability to nicotine addiction and obesity.

Purpose: Adequate preoperative staging of large sessile rectal tumors requires identifying adenomas that already contain an invasive focus, specifically those that are growing in or beyond the submucosa. We systematically compared chromosomal instability patterns in adenoma and carcinoma fractions of the same lesion to assess specific steps in rectal tumor progression. Experimental Design: We analyzed 36 formalin-fixed, paraffin-embedded tumors. Both the adenoma and carcinoma fractions were typed with single nucleotide polymorphism arrays and compared with 21 previously described pure adenomas. Eighteen cases were included in an intratumor heterogeneity analysis. Results: Five specific "malignant" events (gain of 8q, 13q, and 20q and loss of 17p and 18q) and aberrant staining for p53 and SMAD4 were all increased in the adenoma fractions of carcinoma cases compared with pure adenomas. Paired analysis revealed that 31% of the samples had an equal amount of malignant aberrations in their adenoma and carcinoma fractions, whereas 25% had one and 33% had two or more extra malignant events in the carcinoma fraction. Analysis of three core biopsies per patient showed a large degree of intratumor heterogeneity. However, the number of malignant aberrations in the biopsy with the most aberrations per tumor correlated with the corresponding adenoma or carcinoma fraction (r = 0.807; P < 0.001). Conclusion: Five specific chromosomal aberrations, combined with immunohistochemistry for p53 and SMAD4, can predict possible progression of sessile rectal adenomas to early rectal cancer and can, after validation studies, be added to preoperative staging. Preferably, three biopsies should be taken from each tumor to address intratumor heterogeneity.

Contains fulltext : 48736.pdf ( ) (Open Access), BACKGROUND: The molecular determinants of carcinogenesis, tumor progression and patient prognosis can be deduced from simultaneous comparison of thousands of genes by microarray analysis. However, the presence of stroma cells in surgically excised carcinoma tissues might obscure the tumor cell-specific gene expression profiles of these samples. To circumvent this complication, laser microdissection can be performed to separate tumor epithelium from the surrounding stroma and healthy tissue. In this report, we compared RNAs isolated from macrodissected, of which only surrounding healthy tissue had been removed, and microdissected rectal carcinoma samples by microarray analysis in order to determine the most reliable approach to detect the expression of tumor cell-derived genes by microarray analysis. RESULTS: As microdissection yielded low tissue and RNA quantities, extra rounds of mRNA amplification were necessary to obtain sufficient RNA for microarray experiments. These second rounds of amplification influenced the gene expression profiles. Moreover, the presence of stroma cells in macrodissected samples had a minor contribution to the tumor cell gene expression profiles, which can be explained by the observation that more RNA is extracted from tumor epithelial cells than from stroma. CONCLUSION: These data demonstrate that the more convenient procedure of macrodissection can be adequately used and yields reliable data regarding the identification of tumor cell-specific gene expression profiles.