Purpose: Recently, several studies reported a strong functional link between histone deacetylases (HDAC) and the development of tumors
of the large intestine. However, despite the importance of these ...molecules, comparably little is known on expression patterns
and functions of specific HDAC isoforms in colorectal cancer.
Experimental Design: We characterized class I HDAC isoform expression patterns in a cohort of 140 colorectal carcinomas by immunohistochemistry.
In addition, effects of HDAC inhibition by valproic acid and suberoylanilide hydroxamic acid, and specific HDAC isoform knockdown
by short interfering RNA, were investigated in a cell culture model.
Results: We found class I HDACs highly expressed in a subset of colorectal carcinomas with positivity for HDAC1 in 36.4%, HDAC2 in
57.9%, and HDAC3 in 72.9% of cases. Expression was significantly enhanced in strongly proliferating ( P = 0.002), dedifferentiated ( P = 0.022) tumors. High HDAC expression levels implicated significantly reduced patient survival ( P = 0.001), with HDAC2 expression being an independent survival prognosticator (hazard ratio, 2.6; P = 0.03). Short interfering RNA–based inhibition of HDAC1 and HDAC2 but not HDAC3 suppressed growth of colon cancer cells
in vitro , although to a lesser extent than chemical HDAC inhibitors did.
Conclusions: The strong prognostic impact of HDAC isoforms in colorectal cancer, the interactions of HDACs with tumor cell proliferation
and differentiation in vivo , and our finding that HDACs are differentially expressed in colorectal tumors suggest that the evaluation of HDAC expression
in clinical trials for HDAC inhibitors might help to identify a patient subgroup who will exceptionally profit from such a
treatment.
Summary Background Although histone deacetylases (HDACs) are known to have an important regulatory role in cancer cells, and HDAC inhibitors (HDIs) have entered late-phase clinical trials for the ...treatment of several cancers, little is known about the expression patterns of HDAC isoforms in tumours. We aimed to clarify these expression patterns and identify potential diagnostic and prognostic uses of selected class I HDAC isoforms in gastric cancer. Methods Tissue samples from a training cohort and a validation cohort of patients with gastric cancer from two German institutions were used for analyses. Tissue microarrays were generated from tumour tissue collected from patients in the training group, whereas tissue slides were used in the validation group. The tissues were scored for expression of class I HDAC isoforms 1, 2, and 3. Overall expression patterns (gHDAC) were grouped as being negative (all three isoforms negative), partially positive (one or two isoforms positive), or completely positive (all isoforms positive), and correlated with clinicopathological parameters and patient survival. The main endpoints were amount of expression of each of the three HDAC isoforms, patterns of expression of gHDAC, effect of metastasis on expression of HDAC and gHDAC, and overall survival according to HDAC expression patterns. Findings 2617 tissue microarray spots from 143 patients in the training cohort and 606 tissue slides from 150 patients in the validation cohort were studied. 52 of the 143 (36%) gastric tumours in the training cohort and 32 of the 150 (21%) gastric tumours in the validation cohort showed nuclear expression of all three HDAC isoforms. 60 (42%) of tumours in the training cohort and 65 (43%) in the validation cohort expressed one or two isoforms in the nuclei, whereas 31 (22%) of tumours in the training cohort and 53 (35%) in the validation cohort were scored negative for all three proteins. gHDAC expression in both cohorts was higher when lymph-node metastases were present (p=0·0175 for the training group and p=0·0242 for the validation group). Survival data were available for 49 patients in the training group and 123 patients in the validation group. In the validation cohort, 3-year survival was 44% (95% CI 34–57) in the HDAC1-negative group, 50% (39–64) in the HDAC2-negative group, and 48% (34–67) in the gHDAC-negative group. 3-year survival decreased to 21% (11–37) when HDAC1 was positive, 16% (9–31) when HDAC2 was positive, and 5% (1–31) when gHDAC (all isoforms) were positive. Those patients highly expressing one or two isoforms (the gHDAC-intermediate group) had an estimated 3-year survival of 40% (29–56). In multivariate analyses, high gHDAC and HDAC2 expression were associated with shorter survival in the training cohort (gHDAC: hazard ratio HR 4·15 1·23–13·99, p=0·0250; HDAC2: HR 3·58 1·36–9·44, p=0·0100) and in the validation cohort (gHDAC: HR 2·18 1·19–4·01, p=0·0433; HDAC2: HR 1·72 1·08–2·73, p=0·0225), independent of standard clinical predictors. Interpretation High HDAC expression is significantly associated with nodal spread and is an independent prognostic marker for gastric cancer. Additionally, we postulate that immunohistochemical detection of HDAC as a companion diagnostic method might predict treatment response to HDIs, thereby enabling selection of patients for this specific targeted treatment in gastric cancer.
Recent advances in cancer research and diagnostics largely rely on new developments in microscopic or molecular profiling techniques, offering high levels of detail with respect to either spatial or ...molecular features, but usually not both. Here, we present an explainable machine-learning approach for the integrated profiling of morphological, molecular and clinical features from breast cancer histology. First, our approach allows for the robust detection of cancer cells and tumour-infiltrating lymphocytes in histological images, providing precise heatmap visualizations explaining the classifier decisions. Second, molecular features, including DNA methylation, gene expression, copy number variations, somatic mutations and proteins are predicted from histology. Molecular predictions reach balanced accuracies up to 78%, whereas accuracies of over 95% can be achieved for subgroups of patients. Finally, our explainable AI approach allows assessment of the link between morphological and molecular cancer properties. The resulting computational multiplex-histology analysis can help promote basic cancer research and precision medicine through an integrated diagnostic scoring of histological, clinical and molecular features.Cancers are complex diseases that are increasingly studied using a diverse set of omics data. At the same time, histological images show the interaction of cells, which is not visible with bulk omics methods. Binder and colleagues present a method to learn from both kinds of data, such that molecular markers can be associated with visible patterns in the tissue samples and be used for more accurate breast cancer diagnosis.
Background
Risk assessment on the molecular level is important in predictive pathology to determine the risk of metastatic disease for ERpos, HER2neg breast cancer. The gene expression test ...EndoPredict (EP) was trained and validated for prediction of a 10-year risk of distant recurrence to support therapy decisions regarding endocrine therapy alone or in combination with chemotherapy. The EP test provides the 12-gene Molecular Score (MS) and the EPclin-Score (EPclin), which combines the molecular score with tumor size and nodal status. In this project we investigated the correlation of 12-gene MS and EPclin scores with classical pathological markers.
Methods
EndoPredict-based gene expression profiling was performed prospectively in a total of 1652 patients between 2017 and 2020. We investigated tumor grading and Ki67 cut-offs of 20% for binary classification as well as 10% and 30% for three classes (low, intermediate, high), based on national and international guidelines.
Results
410 (24.8%) of 1652 patients were classified as 12-gene MS low risk and 626 (37.9%) as EPclin low risk. We found significant positive associations between 12-gene MS and grading (
p
< 0.001), EPclin and grading (
p
= 0.001), 12-gene MS and Ki67 (
p
< 0.001), and EPclin and Ki67 (
p
< 0.001). However, clinically relevant differences between EP test results, Ki67 and tumor grading were observed. For example, 118 (26.3%) of 449 patients with Ki67 > 20% were classified as low risk by EPclin. Same differences were seen comparing EP test results and tumor grading.
Conclusion
In this study we could show that EP risk scores are distributed differentially among Ki67 expression groups, especially in Ki67 low and high tumors with a substantial proportion of patients with EPclin high risk results in Ki67 low tumors and vice versa. This suggests that classical pathological parameters and gene expression parameters are not interchangeable, but should be used in combination for risk assessment.
The importance of integrating biomarkers into the TNM staging has been emphasized in the 8
Edition of the American Joint Committee on Cancer (AJCC) Staging system. In a pooled analysis of 2148 ...TNBC-patients in the adjuvant setting, TILs are found to strongly up and downstage traditional pathological-staging in the Pathological and Clinical Prognostic Stage Groups from the AJJC 8
edition Cancer Staging System. This suggest that clinical and research studies on TNBC should take TILs into account in addition to stage, as for example patients with stage II TNBC and high TILs have a better outcome than patients with stage I and low TILs.
This pooled analysis aimed to evaluate locoregional recurrence (LRR) rates of breast cancer (BC) after neoadjuvant chemotherapy (NACT) and to identify independent LRR predictors.
10,075 women with ...primary BC from nine neoadjuvant trials were included. The primary outcome was the cumulative incidence rate of LRR as the first event after NACT. Distant recurrence, secondary malignancy or death were defined as competing events. For identifying LRR predictors, surgery type, pathological complete response (pCR), BC subtypes and other potential risk factors were evaluated.
Median followup was 67 months (range 0–215), overall LRR rate was 9.5%, 4.1% in pCR versus 9.5% in non-pCR patients. Younger age, clinically positive lymph nodes, G3 tumours, non-pCR and TNBC but not surgery type were independent LRR predictors in multivariate analysis. Among BC subtypes, 5-year cumulative LRR rates were associated with higher risk in non-pCR versus pCR patients, which was significant for HR+/HER2- (5.9% vs 3.9%; HR = 2.32 95%CI 1.22–4.43; p = 0.011); HR-/HER2+ (14.8% vs 3.1%; HR = 4.26 94%CI 2.35–7.71; p < 0.001) and TNBC (18.5% vs 4.2%; HR = 4.10 95%CI 2.88–5.82; p < 0.001) but not for HR+/HER2+ (8.1% vs 4.8%; HR = 1.56 95%CI 0.85–2.85; p = 0.150). Within non-pCR subgroup, LRR risk was higher for HR-/HER2+ and TNBC vs HR+/HER2- (HR = 2.05 95%CI 1.54–2.73; p < 0.001 and HR = 2.77 95%CI 2.27–3.39; p < 0.001, respectively).
This pooled analysis demonstrated that young age, node-positive and G3 tumours, as well as TNBC, and non-pCR significantly increased the risk of LRR after NACT. Hence, there is a clear need to investigate better multimodality therapies in the post-neoadjuvant setting for high-risk patients.
•Pooled analysis included 10,075 patients with primary breast cancer (BC) after NACT.•Median follow-up was 67 months, 9.5% LRR as first event after NACT were observed.•Predictors of LRR were age, clinical nodal status, tumour grade, BC subtype, pCR.•Patients with HR-/HER2+ and TNBC not achieving pCR were at highest LRR risk.
Harnessing the immune system by checkpoint blockade has greatly expanded the therapeutic options for advanced cancer. Since the efficacy of immunotherapies is influenced by the molecular make-up of ...the tumor and its crosstalk with the immune system, comprehensive analysis of genetic and immunologic tumor characteristics is essential to gain insight into mechanisms of therapy response and resistance. We investigated the association of immune cell contexture and tumor genetics including tumor mutational burden (TMB), copy number alteration (CNA) load, mutant allele heterogeneity (MATH) and specific mutational signatures (MutSigs) using TCGA data of 5722 tumor samples from 21 cancer types. Among all genetic variables, MutSigs associated with DNA repair deficiency and AID/APOBEC gene activity showed the strongest positive correlations with immune parameters. For smoking-related and UV-light-exposure associated MutSigs a few positive correlations were identified, while MutSig 1 (clock-like process) correlated non-significantly or negatively with the major immune parameters in most cancer types. High TMB was associated with high immune cell infiltrates in some but not all cancer types, in contrast, high CNA load and high MATH were mostly associated with low immune cell infiltrates. While a bi- or multimodal distribution of TMB was observed in colorectal, stomach and endometrial cancer where its levels were associated with POLE/POLD1 mutations and MSI status, TMB was unimodal distributed in the most other cancer types including NSCLC and melanoma. In summary, this study uncovered specific genetic-immunology associations in major cancer types and suggests that mutational signatures should be further investigated as interesting candidates for response prediction beyond TMB.
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.
Eight hundred fifty-one pretherapeutic formalin-fixed paraffin-embedded (FFPE) core biopsies from GeparSepto study were sequenced. The panel included 16 genes for mutational (
, and
) and 8 genes for copy-number alteration analysis (
, and
).
The most common genomic alterations were mutations of
(38.4%) and
(21.5%), and 8 different amplifications (
34.9%;
30.6%;
30.1%;
21.9%;
24.1%;
17.7%;
14.9%;
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
-mutated breast cancer
mut: 23.0% vs. wild-type (wt) 38.8%,
< 0.0001 in particular in the HER2pos subcohort multivariate OR = 0.43 (95% CI, 0.24-0.79),
= 0.006. An increased response to nab-paclitaxel was observed only in
wt breast cancer, with univariate significance for the complete cohort (
= 0.009) and the TNBC (
= 0.013) and multivariate significance in the HER2pos subcohort (test for interaction
= 0.0074).
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.
mutations could be a major mediator of therapy resistance in breast cancer.
Molecular subtyping of breast cancer is necessary for therapy selection and mandatory for all breast cancer patients. Metabolic alterations are considered a hallmark of cancer and several metabolic ...drugs are currently being investigated in clinical trials. However, the dependence of metabolic alterations on breast cancer subtypes has not been investigated on -omics scale. Thus, 204 estrogen receptor positive (ER+) and 67 estrogen receptor negative (ER−) breast cancer tissues were investigated using GC–TOFMS based metabolomics. 19 metabolites were detected as altered in a predefined training set (2/3 of tumors) and could be validated in a predefined validation set (1/3 of tumors). The metabolite changes included increases in beta-alanine, 2-hydroyglutarate, glutamate, xanthine and decreases in glutamine in the ER− subtype. Beta-alanine demonstrated the strongest change between ER− and ER+ breast cancer (fold change=2.4, p=1.5E−20). In a correlation analysis with genome-wide expression data in a subcohort of 154 tumors, we found a strong negative correlation (Spearman R=−0.62) between beta-alanine and 4-aminobutyrate aminotransferase (ABAT). Immunohistological analysis confirmed down-regulation of the ABAT protein in ER− breast cancer. In a Kaplan–Meier analysis of a large external expression data set, the ABAT transcript was demonstrated to be a positive prognostic marker for breast cancer (HR=0.6, p=3.2E-15).
It is well-known for more than a decade that breast cancer exhibits distinct gene expression patterns depending on the molecular subtype defined by estrogen receptor (ER) and HER2 status. Here, we show that breast cancer exhibits distinct metabolomics patterns depending on ER status. Our observation supports the current view of ER+ breast cancer and ER− breast as different diseases requiring different treatment strategies.
Metabolic drugs for cancer including glutaminase inhibitors are currently under development and tested in clinical trials. We found glutamate enriched and glutamine reduced in ER− breast cancer compared to ER+ breast cancer and compared to normal breast tissues. Thus, metabolomics analysis highlights the ER− subtype as a preferential target for glutaminase inhibitors.
For the first time, we report on a regulation of beta-alanine catabolism in cancer. In breast cancer, ABAT transcript expression was variable and correlated with ER status. Low ABAT transcript expression was associated with low ABAT protein expression and high beta-alanine concentration. In a large external microarray cohort, low ABAT expression shortened recurrence-free survival in breast cancer, ER+ breast cancer and ER− breast cancer.
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•ER+ breast cancer and ER− breast cancer exhibit distinct metabolite patterns.•The ER− molecular subtype appears as preferential target for glutaminase inhibitors.•Beta-alanine accumulates in breast cancer tissues, especially in the ER− subtype.•ABAT transcript and protein are low in ER− cancer blocking beta-alanine catabolism.•Low ABAT expression shortens survival in ER+ breast cancer and ER− breast cancer.
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