AHRR and SFRP2 in primary versus recurrent high-grade serous ovarian carcinoma and their prognostic implication.

BACKGROUND: The aim of this study was to analyse transcriptomic differences between primary and recurrent high-grade serous ovarian carcinoma (HGSOC) to identify prognostic biomarkers. METHODS: We analysed 19 paired primary and recurrent HGSOC samples using targeted RNA sequencing. We selected the best candidates using in silico survival and pathway analysis and validated the biomarkers using immunohistochemistry on a cohort of 44 paired samples, an additional cohort of 504 primary HGSOCs and explored their function. RESULTS: We identified 233 differential expressed genes. Twenty-three showed a significant prognostic value for PFS and OS in silico. Seven markers (AHRR, COL5A2, FABP4, HMGCS2, ITGA5, SFRP2 and WNT9B) were chosen for validation at the protein level. AHRR expression was higher in primary tumours (p < 0.0001) and correlated with better patient survival (p < 0.05). Stromal SFRP2 expression was higher in recurrent samples (p = 0.009) and protein expression in primary tumours was associated with worse patient survival (p = 0.022). In multivariate analysis, tumour AHRR and SFRP2 remained independent prognostic markers. In vitro studies supported the anti-tumorigenic role of AHRR and the oncogenic function of SFRP2. CONCLUSIONS: Our results underline the relevance of AHRR and SFRP2 proteins in aryl-hydrocarbon receptor and Wnt-signalling, respectively, and might lead to establishing them as biomarkers in HGSOC.

Characterization of Hormone Receptor and HER2 Status in Breast Cancer Using Mass Spectrometry Imaging.

Immunohistochemical evaluation of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor-2 status stratify the different subtypes of breast cancer and define the treatment course. Triple-negative breast cancer (TNBC), which does not register receptor overexpression, is often associated with worse patient prognosis. Mass spectrometry imaging transcribes the molecular content of tissue specimens without requiring additional tags or preliminary analysis of the samples, being therefore an excellent methodology for an unbiased determination of tissue constituents, in particular tumor markers. In this study, the proteomic content of 1191 human breast cancer samples was characterized by mass spectrometry imaging and the epithelial regions were employed to train and test machine-learning models to characterize the individual receptor status and to classify TNBC. The classification models presented yielded high accuracies for estrogen and progesterone receptors and over 95% accuracy for classification of TNBC. Analysis of the molecular features revealed that vimentin overexpression is associated with TNBC, supported by immunohistochemistry validation, revealing a new potential target for diagnosis and treatment.

Clinical and molecular characteristics of HER2-low-positive breast cancer: pooled analysis of individual patient data from four prospective, neoadjuvant clinical trials.

BACKGROUND: The development of anti-HER2 antibody-drug conjugates opens new therapeutic options for patients with breast cancer, including patients with low expression of HER2. To characterise this new breast cancer subtype, we have compared the clinical and molecular characteristics of HER2-low-positive and HER2-zero breast cancer, including response to neoadjuvant chemotherapy and prognosis. METHODS: In this pooled analysis of individual patient data, we evaluated a cohort of 2310 patients with HER2-non-amplified primary breast cancer that were treated with neoadjuvant combination chemotherapy in four prospective neoadjuvant clinical trials (GeparSepto, NCT01583426; GeparOcto, NCT02125344; GeparX, NCT02682693; Gain-2 neoadjuvant, NCT01690702) between July 30, 2012, and March 20, 2019. Central HER2 testing was done prospectively before random assignment of participants in all trials. HER2-low-positive status was defined as immunohistochemistry (IHC) 1+ or IHC2+/in-situ hybridisation negative and HER2-zero was defined as IHC0, based on the American Society of Clinical Oncology/College of American Pathologists guidelines. Disease-free survival and overall survival data were available for 1694 patients (from all trials except GeparX) with a median follow-up of 46·6 months (IQR 35·0-52·3). Bivariable and multivariable logistic regression models and Cox-proportional hazards models were performed based on a predefined statistical analysis plan for analysis of the endpoints pathological complete response, disease-free survival, and overall survival. FINDINGS: A total of 1098 (47·5%) of 2310 tumours were HER2-low-positive and 1212 (52·5%) were HER2-zero. 703 (64·0%) of 1098 patients with HER2-low-positive tumours were hormone receptor positive, compared with 445 (36·7%) of 1212 patients with HER2-zero tumours (p<0.0001). HER2-low-positive tumours had a significantly lower pathological complete response rate than HER2-zero tumours (321 [29·2%] of 1098 vs 473 [39·0%] of 1212, p=0·0002). Pathological complete response was also significantly lower in HER2-low-positive tumours versus HER2-zero tumours in the hormone receptor-positive subgroup (123 [17·5%] of 703 vs 105 [23·6%] of 445, p=0·024), but not in the hormone receptor-negative subgroup (198 [50·1%] of 395 vs 368 [48·0%] of 767, p=0·21). Patients with HER2-low-positive tumours had significantly longer survival than did patients with HER2-zero tumours (3-year disease-free survival: 83·4% [95% CI 80·5-85·9] vs 76·1% [72·9-79·0]; stratified log-rank test p=0·0084; 3-year overall survival: 91·6% [84·9-93·4] vs 85·8% [83·0-88·1]; stratified log-rank test p=0·0016). Survival differences were also seen in patients with hormone receptor-negative tumours (3-year disease-free survival: 84·5% [95% CI 79·5-88·3] vs 74·4% [70·2-78.0]; stratified log-rank test p=0·0076; 3-year overall survival: 90·2% [86·0-93·2] vs 84·3% [80·7-87·3], stratified log-rank test p=0·016), but not in patients with hormone receptor-positive tumours (3-year disease-free survival 82·8% [79·1-85·9] vs 79·3% [73·9-83·7]; stratified log-rank test p=0·39; 3-year overall survival 92·3% [89·6-94·4] vs 88·4% [83·8-91·8]; stratified log-rank test p=0·13). INTERPRETATION: Our results show that HER2-low-positive tumours can be identified as new subgroup of breast cancer by standardised IHC, distinct from HER2-zero tumours. HER2-low-positive tumours have a specific biology and show differences in response to therapy and prognosis, which is particularly relevant in therapy-resistant, hormone receptor-negative tumours. Our results provide a basis for a better understanding of the biology of breast cancer subtypes and the refinement of future diagnostic and therapeutic strategies. FUNDING: German Cancer Aid (Deutsche Krebshilfe).

Tumour-infiltrating lymphocytes and prognosis in different subtypes of breast cancer: a pooled analysis of 3771 patients treated with neoadjuvant therapy.

BACKGROUND: Tumour-infiltrating lymphocytes (TILs) are predictive for response to neoadjuvant chemotherapy in triple-negative breast cancer (TNBC) and HER2-positive breast cancer, but their role in luminal breast cancer and the effect of TILs on prognosis in all subtypes is less clear. Here, we assessed the relevance of TILs for chemotherapy response and prognosis in patients with TNBC, HER2-positive breast cancer, and luminal-HER2-negative breast cancer. METHODS: Patients with primary breast cancer who were treated with neoadjuvant combination chemotherapy were included from six randomised trials done by the German Breast Cancer Group. Pretherapeutic core biopsies from 3771 patients included in these studies were assessed for the number of stromal TILs by standardised methods according to the guidelines of the International TIL working group. TILs were analysed both as a continuous parameter and in three predefined groups of low (0-10% immune cells in stromal tissue within the tumour), intermediate (11-59%), and high TILs (≥60%). We used these data in univariable and multivariable statistical models to assess the association between TIL concentration and pathological complete response in all patients, and between the amount of TILs and disease-free survival and overall survival in 2560 patients from five of the six clinical trial cohorts. FINDINGS: In the luminal-HER2-negative breast cancer subtype, a pathological complete response (pCR) was achieved in 45 (6%) of 759 patients with low TILs, 48 (11%) of 435 with intermediate TILs, and 49 (28%) of 172 with high TILs. In the HER2-positive subtype, pCR was observed in 194 (32%) of 605 patients with low TILs, 198 (39%) of 512 with intermediate TILs, and 127 (48%) of 262 with high TILs. Finally, in the TNBC subtype, pCR was achieved in 80 (31%) of 260 patients with low TILs, 117 (31%) of 373 with intermediate TILs, and 136 (50%) of 273 with high TILs (p<0·0001 for each subtype, χ2 test for trend). In the univariable analysis, a 10% increase in TILs was associated with longer disease-free survival in TNBC (hazard ratio [HR] 0·93 [95% CI 0·87-0·98], p=0·011) and HER2-positive breast cancer (0·94 [0·89-0·99], p=0·017), but not in luminal-HER2-negative tumours (1·02 [0·96-1·09], p=0·46). The increase in TILs was also associated with longer overall survival in TNBC (0·92 [0·86-0·99], p=0·032), but had no association in HER2-positive breast cancer (0·94 [0·86-1·02], p=0·11), and was associated with shorter overall survival in luminal-HER2-negative tumours (1·10 [1·02-1·19], p=0·011). INTERPRETATION: Increased TIL concentration predicted response to neoadjuvant chemotherapy in all molecular subtypes assessed, and was also associated with a survival benefit in HER2-positive breast cancer and TNBC. By contrast, increased TILs were an adverse prognostic factor for survival in luminal-HER2-negative breast cancer, suggesting a different biology of the immunological infiltrate in this subtype. Our data support the hypothesis that breast cancer is immunogenic and might be targetable by immune-modulating therapies. In light of the results in luminal breast cancer, further research investigating the interaction of the immune system with different types of endocrine therapy is warranted. FUNDING: Deutsche Krebshilfe and European Commission.

LRP1B-a prognostic marker in tubo-ovarian high-grade serous carcinoma.

Low-density lipoprotein (LDL) receptor-related protein 1B (LRP1B) is a member of the LDL receptor family and has often been discussed as a tumor suppressor gene, as its down-regulation is correlated with a poor prognosis in multiple carcinoma entities. Due to the high metastasis rate into the fatty peritoneal cavity and current research findings showing a dysregulation of lipid metabolism in tubo-ovarian high-grade serous carcinoma (HGSC), we questioned the prognostic impact of the LRP1B protein expression. We examined a well-characterized large cohort of 571 patients with primary HGSC and analyzed the LRP1B protein expression via immunohistochemical staining (both in tumor and stroma cells separately), performed precise bioimage analysis with QuPath, and calculated the prognostic impact using SPSS. Our results demonstrate that LRP1B functions as a significant prognostic marker for overall survival (OS) and progression-free survival (PFS) in HGSC on the protein level. High cytoplasmic expression of LRP1B in tumor, stroma, and combined tumor and stroma cells has a significantly positive association with a mean prolongation of the OS by 42 months (P = .005), 29 months (P = .005), and 25 months (P = .001), respectively. Additionally, the mean PFS was 18 months longer in tumor (P = .002), 19 months in stroma (P = .004), and 19 months in both cell types combined (P = .01). Our results remained significant in multivariate analysis. We envision LRP1B as a potential prognostic tool that could help us understand the functional role of lipid metabolism in advanced HGSC, especially regarding liposomal medications.

High EVI1 and PARP1 expression as favourable prognostic markers in high-grade serous ovarian carcinoma.

BACKGROUND: Mechanisms of development and progression of high-grade serous ovarian cancer (HGSOC) are poorly understood. EVI1 and PARP1, part of TGF-ß pathway, are upregulated in cancers with DNA repair deficiencies with DNA repair deficiencies and may influce disease progression and survival. Therefore we questioned the prognostic significance of protein expression of EVI1 alone and in combination with PARP1 and analyzed them in a cohort of patients with HGSOC. METHODS: For 562 HGSOC patients, we evaluated EVI1 and PARP1 expression by immunohistochemical staining on tissue microarrays with QuPath digital semi-automatic positive cell detection. RESULTS: High EVI1 expressing (> 30% positive tumor cells) HGSOC were associated with improved progression-free survival (PFS) (HR = 0.66, 95% CI: 0.504-0.852, p = 0.002) and overall survival (OS) (HR = 0.45, 95% CI: 0.352-0.563, p < 0.001), including multivariate analysis. Most interestingly, mutual high expression of both proteins identifies a group with particularly good prognosis. Our findings were proven technically and clinically using bioinformatical data sets for single-cell sequencing, copy number variation and gene as well as protein expression. CONCLUSIONS: EVI1 and PARP1 are robust prognostic biomarkers for favorable prognosis in HGSOC and imply further research with respect to their reciprocity.

RANK Expression as an Independent Predictor for Response to Neoadjuvant Chemotherapy in Luminal-Like Breast Cancer: A Translational Insight from the GeparX Trial.

PURPOSE: The GeparX study investigated whether denosumab as add-on treatment to nab-paclitaxel-based neoadjuvant chemotherapy (NACT) with two different schedules (125 mg/m² weekly vs. day 1, 8 every 22 days) may increase pathologic complete response (pCR) rate. The addition of denosumab to NACT did not improve pCR rates as recently published. In this study, we investigated whether receptor activator of nuclear factor-kappa B (RANK) expression, as part of the denosumab target pathway: (i) may retrospectively identify a subgroup of patients with additional clinical benefit of denosumab or (ii) may predict response to nab-paclitaxel NACT. EXPERIMENTAL DESIGN: RANK protein was IHC-stained on pre-therapeutic core biopsies from patients of the GeparX study (n = 667) with the antibody RANK/Envision System HRP (DAB) and was analyzed for the percentage of membranous RANK tumor cell staining (>5% RANKhigh vs. ≤5% RANKlow). RESULTS: We could not identify any patient subgroup with differential response under denosumab add-on treatment in patients with RANKhigh expression [139/667, 20.8%; OR, 0.86; 95% confidence interval (CI), 0.44-1.68; P = 0.667] or RANKlow expression (528/667 (79.2%) OR, 1.10; 95% CI, 0.78-1.56; P = 0.589; Pinteraction = 0.528). However, the pCR rate was higher in the RANKhigh subgroup compared with RANKlow (50% vs. 39%; OR, 1.52; 95% CI, 1.04-2.21; P = 0.037). RANK expression constituted an independent predictor of response to NACT frequently in patients with luminal-like subtype (HR+/HER2-; OR, 2.98; 95% CI, 1.30-6.79; P = 0.010). No predictive value of RANK expression among the different nab-paclitaxel regimens was observed. CONCLUSION: We report RANK expression to be an independent predictive biomarker for response to NACT in patients with luminal-like breast cancer.

Increased expression of IDO1 is associated with improved survival and increased number of TILs in patients with high-grade serous ovarian cancer.

BACKGROUND: The enzyme indoleamine 2,3-dioxygenase 1 (IDO1) plays a crucial role in regulating the immune system's response to tumors, but its exact role in cancer, especially in high-grade serous ovarian cancer (HGSOC), remains controversial. We aimed to investigate the prognostic impact of IDO1 expression and its correlation with tumor-infiltrating lymphocytes (TILs) in HGSOC. METHODS: Immunohistochemical (IHC) staining and bioimage analysis using the QuPath software were employed to assess IDO1 protein expression in a well-characterized cohort of 507 patients with primary HGSOC. Statistical evaluation was performed using SPSS, and in silico validation considering IDO1 mRNA expression in bulk and single-cell gene expression datasets was conducted. Additionally, IDO1 expression in interferon-gamma (IFNG) stimulated HGSOC cell lines was analyzed. RESULTS: Our findings revealed that IDO1 protein and mRNA expression serve as positive prognostic markers for overall survival (OS) and progression-free survival (PFS) in HGSOC. High IDO1 expression was associated with a significant improvement in OS by 21 months (p < 0.001) and PFS by 6 months (p = 0.016). Notably, elevated IDO1 expression correlated with an increased number of CD3+ (p < 0.001), CD4+ (p < 0.001), and CD8+ TILs (p < 0.001). Furthermore, high IDO1 mRNA expression and protein level were found to be associated with enhanced responsiveness to pro-inflammatory cytokines, particularly IFNG. CONCLUSIONS: Our study provides evidence that IDO1 expression serves as a positive prognostic marker in HGSOC and is associated with an increased number of CD3+, CD4+ and CD8+ TILs. Understanding the intricate relationship between IDO1, TILs, and the tumor microenvironment may hold the key to improving outcomes in HGSOC.

Tumor Growth Rate Estimates Are Independently Predictive of Therapy Response and Survival in Recurrent High-Grade Serous Ovarian Cancer Patients.

This study aimed to assess the predictive value of tumor growth rate estimates based on serial cancer antigen-125 (CA-125) levels on therapy response and survival of patients with recurrent high-grade serous ovarian cancer (HGSOC). In total, 301 consecutive patients with advanced HGSOC (exploratory cohort: n = 155, treated at the Medical University of Vienna; external validation cohort: n = 146, from the Ovarian Cancer Therapy-Innovative Models Prolong Survival (OCTIPS) consortium) were enrolled. Tumor growth estimates were obtained using a validated two-phase equation model involving serial CA-125 levels, and their predictive value with respect to treatment response to the next chemotherapy and the prognostic value with respect to disease-specific survival and overall survival were assessed. Tumor growth estimates were an independent predictor for response to second-line chemotherapy and an independent prognostic factor for second-line chemotherapy use in both univariate and multivariable analyses, outperforming both the predictive (second line: p = 0.003, HR 5.19 [1.73-15.58] vs. p = 0.453, HR 1.95 [0.34-11.17]) and prognostic values (second line: p = 0.042, HR 1.53 [1.02-2.31] vs. p = 0.331, HR 1.39 [0.71-2.27]) of a therapy-free interval (TFI) < 6 months. Tumor growth estimates were a predictive factor for response to third- and fourth-line chemotherapy and a prognostic factor for third- and fourth-line chemotherapy use in the univariate analysis. The CA-125-derived tumor growth rate estimate may be a quantifiable and easily assessable surrogate to TFI in treatment decision making for patients with recurrent HGSOC.

Classification of Molecular Subtypes of High-Grade Serous Ovarian Cancer by MALDI-Imaging.

Despite the correlation of clinical outcome and molecular subtypes of high-grade serous ovarian cancer (HGSOC), contemporary gene expression signatures have not been implemented in clinical practice to stratify patients for targeted therapy. Hence, we aimed to examine the potential of unsupervised matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) to stratify patients who might benefit from targeted therapeutic strategies. Molecular subtyping of paraffin-embedded tissue samples from 279 HGSOC patients was performed by NanoString analysis (ground truth labeling). Next, we applied MALDI-IMS paired with machine-learning algorithms to identify distinct mass profiles on the same paraffin-embedded tissue sections and distinguish HGSOC subtypes by proteomic signature. Finally, we devised a novel approach to annotate spectra of stromal origin. We elucidated a MALDI-derived proteomic signature (135 peptides) able to classify HGSOC subtypes. Random forest classifiers achieved an area under the curve (AUC) of 0.983. Furthermore, we demonstrated that the exclusion of stroma-associated spectra provides tangible improvements to classification quality (AUC = 0.988). Moreover, novel MALDI-based stroma annotation achieved near-perfect classifications (AUC = 0.999). Here, we present a concept integrating MALDI-IMS with machine-learning algorithms to classify patients according to distinct molecular subtypes of HGSOC. This has great potential to assign patients for personalized treatment.

DNA methylation profiling identifies two distinct subgroups in breast cancers with low hormone receptor expression, mainly associated with HER2 amplification status.

BACKGROUND: Current clinical guidelines suggest that breast cancers with low hormone receptor expression (LowHR) in 1-10% of tumor cells should be regarded as hormone receptor positive. However, clinical data show that these patients have worse outcome compared to patients with hormone receptor expression above 10%. We performed DNA methylation profiling on 23 LowHR breast cancer specimens, including 13 samples with HER2 amplification and compared our results with a reference breast cancer cohort from The Cancer Genome Atlas to clarify the status for this infrequent but important patient subgroup. RESULTS: In unsupervised clustering and dimensionality reduction, breast cancers with low hormone receptor expression that lacked HER2 amplification usually clustered with triple negative breast cancer (TNBC) reference samples (8/10; "LowHR TNBC-like"). In contrast, most specimens with low hormone receptor expression and HER2 amplification grouped with hormone receptor positive cancers (11/13; "LowHR HRpos-like"). We observed highly similar DNA methylation patterns of LowHR TNBC-like samples and true TNBCs. Furthermore, the Ki67 proliferation index of LowHR TNBC-like samples and clinical outcome parameters were more similar to TNBCs and differed from LowHR HRpos-like cases. CONCLUSIONS: We here demonstrate that LowHR breast cancer comprises two epigenetically distinct groups. Our data strongly suggest that LowHR TNBC-like samples are molecularly, histologically and clinically closely related to TNBC, while LowHR HRpos-like specimens are closely related to hormone receptor positive tumors.

Immune-related Gene Expression Predicts Response to Neoadjuvant Chemotherapy but not Additional Benefit from PD-L1 Inhibition in Women with Early Triple-negative Breast Cancer.

PURPOSE: We evaluated mRNA signatures to predict response to neoadjuvant PD-L1 inhibition in combination with chemotherapy in early triple-negative breast cancer. EXPERIMENTAL DESIGN: Targeted mRNA sequencing of 2,559 transcripts was performed in formalin-fixed, paraffin-embedded samples from 162 patients of the GeparNuevo trial. We focused on validation of four predefined gene signatures and differential gene expression analyses for new predictive markers. RESULTS: Two signatures [GeparSixto signature (G6-Sig) and IFN signature (IFN-Sig)] were predictive for treatment response in a multivariate model including treatment arm [G6-Sig: OR, 1.558; 95% confidence interval (CI), 1.130-2.182; P = 0.008 and IFN-Sig: OR, 1.695; 95% CI, 1.234-2.376; P = 0.002), while the CYT metric predicted pathologic complete response (pCR) in the durvalumab arm, and the proliferation-associated gene signature in the placebo arm. Expression of PD-L1 mRNA was associated with better response in both arms, indicating that increased levels of PD-L1 are a general predictor of neoadjuvant therapy response. In an exploratory analysis, we identified seven genes that were higher expressed in responders in the durvalumab arm, but not the placebo arm: HLA-A, HLA-B, TAP1, GBP1, CXCL10, STAT1, and CD38. These genes were associated with cellular antigen processing and presentation and IFN signaling. CONCLUSIONS: Immune-associated signatures are associated with pCR after chemotherapy, but might be of limited use for the prediction of response to additional immune checkpoint blockade. Gene expressions related to antigen presentation and IFN signaling might be interesting candidates for further evaluation.

Therapy response and prognosis of patients with early breast cancer with low positivity for hormone receptors - An analysis of 2765 patients from neoadjuvant clinical trials.

AIM: To evaluate HER2-negative breast cancer (BC) with a low hormone receptor (HR) expression, with regard to pathological complete response (pCR) and survival, in comparison to triple-negative BC (TNBC) and strong HR-positive BC. METHODS: We compared negative [oestrogen (ER) and progesterone receptor (PR) <1%], low-positive (ER and/or PR 1-9%) and strong-positive (ER or PR 10-100%) HR-expression in neoadjuvant clinical trial cohorts (n = 2765) of BC patients. End-points were disease-free survival (DFS), distant-disease free survival (DDFS) and overall survival (OS). We performed RNA sequencing on available tumour tissue samples from patients with low-HR expression (n = 38). RESULTS: Ninety-four (3.4%) patients had low HR-positive tumours, 1769 (64.0%) had strong HR-positive tumours, and 902 (32.6%) had TNBC. There were no significant differences in pCR rates between women with low HR-positive tumours (27.7%) and women with TNBC (35.5%). DFS and DDFS were also not different [for DFS, hazard ratio 1.26, 95%-CI (confidence interval) : 0.87-1.83, log-rank test p = 0.951; for DDFS, hazard ratio 1.17, 95%-CI: 0.78-1.76, log-rank test p = 0.774]. Patients with strong HR-positive tumours had a significantly lower pCR rate (pCR 9.4%; odds ratio 0.38, 95%-CI: 0.23-0.63), but better DFS (hazard ratio 0.48, 95%-CI: 0.33-0.70) and DDFS (hazard ratio 0.49, 95%-CI: 0.33-0.74) than patients with low HR-positive tumours. Molecular subtyping (RNA sequencing) of low HR-positive tumours classified these predominantly into a basal subtype (86.8%). CONCLUSION: Low HR-positive, HER2-negative tumours have a similar clinical behaviour to TNBC showing high pCR rates and poor survival and also a basal-like gene expression signature. Patients with low HR-positive tumours should be regarded as candidates for therapy strategies targeting TNBC.

MALDI-Imaging for Classification of Epithelial Ovarian Cancer Histotypes from a Tissue Microarray Using Machine Learning Methods.

PURPOSE: Precise histological classification of epithelial ovarian cancer (EOC) has immanent diagnostic and therapeutic consequences, but remains challenging in histological routine. The aim of this pilot study is to examine the potential of matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry in combination with machine learning methods to classify EOC histological subtypes from tissue microarray. EXPERIMENTAL DESIGN: Formalin-fixed-paraffin-embedded tissue of 20 patients with ovarian clear-cell, 14 low-grade serous, 19 high-grade serous ovarian carcinomas, and 14 serous borderline tumors are analyzed using MALDI-Imaging. Classifications are computed by linear discriminant analysis (LDA), support vector machines with linear (SVM-lin) and radial basis function kernels (SVM-rbf), a neural network (NN), and a convolutional neural network (CNN). RESULTS: MALDI-Imaging and machine learning methods result in classification of EOC histotypes with mean accuracy of 80% for LDA, 80% SVM-lin, 74% SVM-rbf, 83% NN, and 85% CNN. Based on sensitivity (69-100%) and specificity (90-99%), CCN and NN are most suited to EOC classification. CONCLUSION AND CLINICAL RELEVANCE: The pilot study demonstrates the potential of MALDI-Imaging derived proteomic classifiers in combination with machine learning algorithms to discriminate EOC histotypes. Applications may support the development of new prognostic parameters in the assessment of EOC.

Mutational Diversity and Therapy Response in Breast Cancer: A Sequencing Analysis in the Neoadjuvant GeparSepto Trial.

PURPOSE: Next-generation sequencing (NGS) can be used for comprehensive investigation of molecular events in breast cancer. We evaluated the relevance of genomic alterations for response to neoadjuvant chemotherapy (NACT) in the GeparSepto trial. EXPERIMENTAL DESIGN: Eight hundred fifty-one pretherapeutic formalin-fixed paraffin-embedded (FFPE) core biopsies from GeparSepto study were sequenced. The panel included 16 genes for mutational (AKT1, BRAF, CDH1, EGFR, ERBB2, ESR1, FBXW7, FGFR2, HRAS, KRAS, NRAS, SF3B1, TP53, HNF1A, PIK3CA, and PTEN) and 8 genes for copy-number alteration analysis (CCND1, ERBB2, FGFR1, PAK1, PIK3CA, TOP2A, TP53, and ZNF703). RESULTS: The most common genomic alterations were mutations of TP53 (38.4%) and PIK3CA (21.5%), and 8 different amplifications (TOP2A 34.9%; ERBB2 30.6%; ZNF703 30.1%; TP53 21.9%; PIK3CA 24.1%; CCND1 17.7%; PAK1 14.9%; FGFR 12.6%). All other alterations had a prevalence of less than 5%. The genetic heterogeneity in different breast cancer subtypes [lum/HER2neg vs. HER2pos vs. triple-negative breast cancer (TNBC)] was significantly linked to differences in NACT response. A significantly reduced pathologic complete response rate was observed in PIK3CA-mutated breast cancer [PIK3CAmut: 23.0% vs. wild-type (wt) 38.8%, P < 0.0001] in particular in the HER2pos subcohort [multivariate OR = 0.43 (95% CI, 0.24-0.79), P = 0.006]. An increased response to nab-paclitaxel was observed only in PIK3CAwt breast cancer, with univariate significance for the complete cohort (P = 0.009) and the TNBC (P = 0.013) and multivariate significance in the HER2pos subcohort (test for interaction P = 0.0074). CONCLUSIONS: High genetic heterogeneity was observed in different breast cancer subtypes. Our study shows that FFPE-based NGS can be used to identify markers of therapy resistance in clinical study cohorts. PIK3CA mutations could be a major mediator of therapy resistance in breast cancer.

NAB-Paclitaxel Improves Disease-Free Survival in Early Breast Cancer: GBG 69-GeparSepto.

PURPOSE: The GeparSepto trial demonstrated that weekly nanoparticle albumin-bound (NAB)-paclitaxel significantly improves the pathologic complete remission rate compared with weekly solvent-based (sb) paclitaxel followed by epirubicin plus cyclophosphamide as neoadjuvant treatment in patients with primary breast cancer (BC). Here, we report data on long-term outcomes. METHODS: Patients with histologically confirmed primary BC were randomly assigned in a 1:1 ratio to 12 times weekly NAB-paclitaxel 150 mg/m2 (after study amendment, 125 mg/m2) or weekly sb-paclitaxel 80 mg/m2 followed in both arms by four times epirubicin 90 mg/m2 plus cyclophosphamide 600 mg/m2 every 3 weeks. Patients with human epidermal growth factor receptor 2 (HER2)-positive BC received dual antibody treatment with trastuzumab (8 mg/kg loading dose followed by 6 mg/kg every 3 weeks) and pertuzumab (840 mg loading dose followed by 420 mg every 3 weeks) concurrently to chemotherapy and continued for 1 year. RESULTS: A total of 1,206 patients started treatment, 606 with NAB-paclitaxel and 600 with sb-paclitaxel. After a median follow-up of 49.6 months (range, 0.5 to 64.0 months), 243 invasive disease-free survival (iDFS) events were reported (143 in the sb-paclitaxel and 100 in the NAB-paclitaxel arm). At 4 years, overall patients treated with NAB-paclitaxel had a significantly better iDFS compared with sb-paclitaxel (84.0% v 76.3%; hazard ratio, 0.66; 95% CI, 0.51 to 0.86; P = .002), whereas overall survival did not significantly differ between the two treatment arms (89.7% v 87.2%, respectively; hazard ratio, 0.82; 95% CI, 0.59 to 1.16; P = .260). Long-term follow-up of the treatment-related peripheral sensory neuropathy (PSN) showed a significant decrease of the median time to resolve PSN after NAB-paclitaxel 125 mg/m2 compared with NAB-paclitaxel 150 mg/m2. CONCLUSION: The significantly higher pathologic complete response rate with NAB-paclitaxel translated into a significantly improved iDFS in patients with early BC as compared with sb-paclitaxel. PSN improved much faster under NAB-paclitaxel 125 mg/m2 compared with NAB-paclitaxel 150 mg/m2.

Relevance of tumour-infiltrating lymphocytes, PD-1 and PD-L1 in patients with high-risk, nodal-metastasised breast cancer of the German Adjuvant Intergroup Node-positive study.

BACKGROUND: Immune cell infiltration in breast cancer is important for the patient's prognosis and response to systemic therapies including immunotherapy. We sought to investigate the prevalence of tumour-infiltrating lymphocytes (TILs) and their association with immune checkpoints such as programmed cell death 1 (PD-1) and programmed cell death ligand 1 (PD-L1) in high-risk, node-positive breast cancer of the adjuvant German Adjuvant Intergroup Node-positive (GAIN-1) trial. PATIENTS AND METHODS: We evaluated TILs by haematoxylin and eosin staining and PD-1 and PD-L1 (SP263 assay) expression by immunohistochemistry in 1318 formalin-fixed, paraffin-embedded breast carcinomas. The association of TILs with PD-1, PD-L1, molecular intrinsic subtypes, outcome and therapy regimens (dose-dense [dd] epirubicin, paclitaxel and cyclophosphamide [EPC] and dd epirubicin, cyclophosphamide, paclitaxel and capecitabine [EC-PwX]) was statistically tested. RESULTS: Overall TILs density was significantly associated with the expression of PD-1 and PD-L1 in immune cells (each p < 0.0001) and PD-L1 in tumour cells (p = 0.0051). TILs were more common in triple-negative breast cancer (TNBC) and human epidermal growth factor receptor 2 (HER2)-positive tumours (each p < 0.0001). On multivariate Cox regression analyses, patients with breast cancer without TILs had an unfavourable disease-free survival (DFS) in the EPC arm compared with the EC-PwX arm (hazard ratio [HR] = 0.69 [0.44-1.06], p = 0.0915); but no differences were seen in tumours with TILs (HR = 1.24 [0.92-1.67], p = 0.1566, interaction p = 0.0336). PD-1-positive immune cells in TNBC were associated with a significantly better DFS (HR = 0.50 [0.25-0.99], p = 0.0457). PD-L1 expression had no impact on patient outcome. CONCLUSIONS: TILs predict the benefit of intensified ddEPC compared with ddEC-PwX therapy in node-positive, high-risk breast cancer. TILs, PD-1 and PD-L1 are linked to each other indicating tumour immunogenicity. Moreover, PD-1-positive immune cells have a positive prognostic impact in TNBC. CLINICAL TRIAL: NCT00196872.

High-grade ovarian serous carcinoma patients exhibit profound alterations in lipid metabolism.

Ovarian cancer is a very severe type of disease with poor prognosis. Treatment of ovarian cancer is challenging because of the lack of tests for early detection and effective therapeutic targets. Thus, new biomarkers are needed for both diagnostics and better understanding of the cellular processes of the disease. Small molecules, consisting of metabolites or lipids, have shown emerging potential for ovarian cancer diagnostics. Here we performed comprehensive lipidomic profiling of serum and tumor tissue samples from high-grade serous ovarian cancer patients to find lipids that were altered due to cancer and also associated with progression of the disease. Ovarian cancer patients exhibited an overall reduction of most lipid classes in their serum as compared to a control group. Despite the overall reduction, there were also specific lipids showing elevation, and especially alterations in ceramide and triacylglycerol lipid species were dependent on their fatty acyl side chain composition. Several lipids showed progressive alterations in patients with more advanced disease and poorer overall survival, and outperformed CA-125 as prognostic markers. The abundance of many serum lipids correlated with their abundance in tumor tissue samples. Furthermore, we found a negative correlation of serum lipids with 3-hydroxybutyric acid, suggesting an association between decreased lipid levels and fatty acid oxidation. In conclusion, here we present a comprehensive analysis of lipid metabolism alterations in ovarian cancer patients, with clinical implications.

Transposable elements--is there a link between evolution and cancer?

Currently, the most predominant theory concerning the formation of cancer is that it is a genetic accident. Accordingly, various agents are thought to cause DNA damage which then subsequently activates oncogenes and inactivates tumor suppressor genes. This article, however, describes a theory that interprets cancer as a misguided adaptation. Stressors, which cannot be compensated for with the usual cell possibilities might arouse evolutionary mechanisms intended to create new protein variants. One of these is the activation of transposable elements which leads to a reformatting of the genome. The result of this process is either a cell that survives very well under stress (and will, therefore, never be detected), a dead cell (in case the process is ineffective), or a more or less abnormal and harmful cell that builds up a new but cancerous organ. This theory explains the complex genetic alterations which are present in almost all cancer cells. It also explains the action of non-mutagenic carcinogens. As part of the reformatting process of the cancer cell genome, activation of oncogenes and inactivation of tumor suppressor genes are not stochastic events but the result of an unlucky genomic composition.

Accumulated Metabolites of Hydroxybutyric Acid Serve as Diagnostic and Prognostic Biomarkers of Ovarian High-Grade Serous Carcinomas.

Ovarian cancer is a heterogeneous disease of low prevalence, but poor survival. Early diagnosis is critical for survival, but it is often challenging because the symptoms of ovarian cancer are subtle and become apparent only during advanced stages of the disease. Therefore, the identification of robust biomarkers of early disease is a clinical priority. Metabolomic profiling is an emerging diagnostic tool enabling the detection of biomarkers reflecting alterations in tumor metabolism, a hallmark of cancer. In this study, we performed metabolomic profiling of serum and tumor tissue from 158 patients with high-grade serous ovarian cancer (HGSOC) and 100 control patients with benign or non-neoplastic lesions. We report metabolites of hydroxybutyric acid (HBA) as novel diagnostic and prognostic biomarkers associated with tumor burden and patient survival. The accumulation of HBA metabolites caused by HGSOC was also associated with reduced expression of succinic semialdehyde dehydrogenase (encoded by ALDH5A1), and with the presence of an epithelial-to-mesenchymal transition gene signature, implying a role for these metabolic alterations in cancer cell migration and invasion. In conclusion, our findings represent the first comprehensive metabolomics analysis in HGSOC and propose a new set of metabolites as biomarkers of disease with diagnostic and prognostic capabilities.