Analytical validation of the PAM50-based Prosigna Breast Cancer Prognostic Gene Signature Assay and nCounter Analysis System using formalin-fixed paraffin-embedded breast tumor specimens

Exploring the diagnostic potential of miRNA signatures in the Fabry disease serum: A comparative study of automated and manual sample isolations

Fabry disease, an X-linked lysosomal storage disorder caused by galactosidase α (GLA) gene mutations, exhibits diverse clinical manifestations, and poses significant diagnostic challenges. Early diagnosis and treatment are crucial for improved patient outcomes, pressing the need for reliable biomarkers. In this study, we aimed to identify miRNA candidates as potential biomarkers for Fabry disease using the KingFisher™ automated isolation method and NanoString nCounter® miRNA detection assay. Clinical serum samples were collected from both healthy subjects and Fabry disease patients. RNA extraction from the samples was performed using the KingFisher™ automated isolation method with the MagMAX mirVanaTM kit or manually using the Qiagen miRNeasy kit. The subsequent NanoString nCounter® miRNA detection assay showed consistent performance and no correlation between RNA input concentration and raw count, ensuring reliable and reproducible results. Interestingly, the detection range and highly differential miRNA between the control and disease groups were found to be distinct depending on the isolation method employed. Nevertheless, enrichment analysis of miRNA-targeting genes consistently revealed significant associations with angiogenesis pathways in both isolation methods. Additionally, our investigation into the impact of enzyme replacement therapy on miRNA expression indicated that some differential miRNAs may be sensitive to treatment. Our study provides valuable insights to identify miRNA biomarkers for Fabry disease. While different isolation methods yielded various detection ranges and highly differential miRNAs, the consistent association with angiogenesis pathways suggests their significance in disease progression. These findings lay the groundwork for further investigations and validation studies, ultimately leading to the development of non-invasive and reliable biomarkers to aid in early diagnosis and treatment monitoring for Fabry disease.

Prosigna Assay for Treatment Decisions in Early Breast Cancer: A Decision Impact Study

Introduction: Therapeutic decisions in early breast cancer are based on clinico-pathological features which are subject to intra- and inter-observer variability. This single-center decision impact study aimed to evaluate the effects of the Prosigna assay on physicians' adjuvant treatment choices. Methods: Between 09/2017 and 02/2018, formalin-fixed tumor samples from 52 newly diagnosed, postmenopausal, hormone receptor-positive, HER2-negative breast cancer (T1-T2; pN0-N1a) patients were analyzed. Pre-test clinical judgements and Prosigna test results were compared. Results: The mean age was 59 (42-77). Invasive ductal carcinoma (79.2%), grade 2 (52.8%) and T1c-N0 tumors (43.4%) were represented. There was 40.4% discordance between the pre- and post-test risk of recurrences. No significant change was observed in the clinical intermediate risk category, while there was a net reclassification of low-risk patients into a high Prosigna recurrence risk group. In addition, clinically determined intrinsic subtypes were 34.6% discordant with the Prosigna results, which is largely driven by the reclassification of the luminal A tumors into the Prosigna-assessed luminal B group. Before the Prosigna test, endocrine treatment was the primary choice in 20 patients (39.2%), and chemotherapy was recommended to 31 patients (60.8%). Overall, the Prosigna assay led to a change in treatment choice for one patient. Conclusions: Although conventional risk assessment methods are relatively inexpensive with shorter turnaround times, their accuracy and value for risk reduction are suboptimal. According to our results, the Prosigna assay was found to be a relevant tool for the clinical decision making process. Long-term follow-up of these patients will elucidate the potential benefits of using multigene molecular tests as biomarkers for treatment.

Age-Related Clusters and Favorable Immune Phenotypes in Young Breast Cancer Patients

Breast cancer (BC) patients aged <40 years at diagnosis experience aggressive disease and poorer survival compared with women diagnosed with BC at 40 to 49 years, but the age-related biology is described to little extent. Here, we explored transcriptional alterations in BC to gain better understanding of age-related tumor biology. We studied a subset of the Bergen in-house cohort (n = 127; age range, 26-49 years) and used the NanoString Breast Cancer 360 expression panel on formalin-fixed paraffin-embedded BC tissue, and publicly available global BC messenger RNA expression data (n = 204; age range, 22-49 years), to explore differentially expressed genes between the young (age <40 years) and older (age 40-49 years) patients. Unsupervised hierarchical clustering was applied to identify gene expression-based patient clusters. We applied established computational approaches to define the PAM50 subtypes, risk of recurrence scores (ROR), and risk groups and to infer the proportions of 22 immune cell types from bulk gene expression profiles of patients aged <50 years at BC diagnosis. Differentially expressed genes and gene sets were investigated using OncoEnrichR and g:Profiler to describe functional profiles and pathway enrichment. We identified 4 age-related patient clusters presenting distinct characteristics of PAM50 subtypes and ROR profiles, which demonstrated independent prognostic value when adjusted for traditional clinicopathologic variables and the known molecular subtypes. Our findings showed better survival than expected in the basal-enriched cluster 2 and in triple-negative and basal-like BC. Deconvolution analyses of immunophenotypes indicated higher levels of M0 and M1 macrophages than M2 macrophages in subsets of young BC. Our approach identifies age-based patient clusters with distinct clinicopathologic profiles, to a large extent overlapping with the PAM50 subtypes, although with independent prognostic values in multivariate survival analyses. The patient clusters provided new insight in the immune cell distribution across tumor subtypes, potentially contributing to survival differences between the clusters and the molecular subtypes and indicating age-related mechanisms improving outcome. Our study confirms the applicability of ROR as a valid prognosticator also in a young BC cohort.

Selecting postoperative adjuvant systemic therapy for early-stage breast cancer: An updated assessment and systematic review of leading commercially available gene expression assays

Gene expression assays (GEAs) can guide treatment for early-stage breast cancer. Several large prospective randomized clinical trials, and numerous additional studies, now provide new information for selecting an appropriate GEA. This systematic review builds upon prior reviews, with a focus on five widely commercialized GEAs (Breast Cancer Index®, EndoPredict®, MammaPrint®, Oncotype DX®, and Prosigna®). The comprehensive dataset available provides a contemporary opportunity to assess each GEA's utility as a prognosticator and/or predictor of adjuvant therapy benefit.

RNA biomarkers in cancer therapeutics: The promise of personalized oncology

Cancer therapy is a rapidly evolving and constantly expanding field. Current approaches include surgery, conventional chemotherapy and novel biologic agents as in immunotherapy, that together compose a wide armamentarium. The plethora of choices can, however, be clinically challenging in prescribing the most suitable treatment for any given patient. Fortunately, biomarkers can greatly facilitate the most appropriate selection. In recent years, RNA-based biomarkers have proven most promising. These molecules that range from small noncoding RNAs to protein coding gene transcripts can be valuable in cancer management and especially in cancer therapeutics. Compared to their DNA counterparts which are stable throughout treatment, RNA-biomarkers are dynamic. This allows prediction of success prior to treatment start and can identify alterations in expression that could reflect response. Moreover, improved nucleic acid technology allows RNA to be extracted from practically every biofluid/matrix and evaluated with exceedingly high analytic sensitivity. In addition, samples are largely obtained by minimally invasive procedures and as such can be used serially to assess treatment response real-time. This chapter provides the reader insight on currently known RNA biomarkers, the latest research employing Artificial Intelligence in the identification of such molecules and in clinical decisions driving forward the era of personalized oncology.

Analytical validation of HER2DX genomic test for early-stage HER2-positive breast cancer

BACKGROUND

HER2DX, a multianalyte genomic test, has been clinically validated to predict breast cancer recurrence risk (relapse risk score), the probability of achieving pathological complete response post-neoadjuvant therapy (pCR likelihood score), and individual ERBB2 messenger RNA (mRNA) expression levels in patients with early-stage human epidermal growth factor receptor 2 (HER2)-positive breast cancer. This study delves into the comprehensive analysis of HER2DX's analytical performance.

MATERIALS AND METHODS

Precision and reproducibility of HER2DX risk, pCR, and ERBB2 mRNA scores were assessed within and between laboratories using formalin-fixed paraffin-embedded (FFPE) tumor tissues and purified RNA. Robustness was appraised by analyzing the impact of tumor cell content and protocol variations including different instruments, reagent lots, and different RNA extraction kits. Variability was evaluated across intratumor biopsies and genomic platforms [RNA sequencing (RNAseq) versus nCounter], and according to protocol variations.

RESULTS

Precision analysis of 10 FFPE tumor samples yielded a maximal standard error of 0.94 across HER2DX scores (1-99 scale). High reproducibility of HER2DX scores across 29 FFPE tumors and 20 RNAs between laboratories was evident (correlation coefficients >0.98). The probability of identifying score differences >5 units was ≤5.2%. No significant variability emerged based on platform instruments, reagent lots, RNA extraction kits, or TagSet thaw/freeze cycles. Moreover, HER2DX displayed robustness at low tumor cell content (10%). Intratumor variability across 212 biopsies (106 tumors) was <4.0%. Concordance between HER2DX scores from 30 RNAs on RNAseq and nCounter platforms exceeded 90.0% (Cohen's κ coefficients >0.80).

CONCLUSIONS

The HER2DX assay is highly reproducible and robust for the quantification of recurrence risk, pCR likelihood, and ERBB2 mRNA expression in early-stage HER2-positive breast cancer.

Omission of radiation therapy post breast conserving surgery

Radiation therapy (RT) after breast conserving surgery decreases the risks of local recurrence and breast cancer mortality in the multidisciplinary management of patients with breast cancer. However, breast cancer is a heterogeneous disease, and the absolute benefit of post-operative RT in individual patients varies substantially. Clinical trials aiming to identify patients with low-risk early breast cancer in whom post-operative RT may be safely omitted, based on conventional clinical-pathologic variables alone, have not provided sufficiently tailored information on local recurrence risk assessment to guide treatment decisions. The majority of patients with early breast cancer continue to be routinely treated with RT after breast conserving surgery. This approach may represent over-treatment for a substantial proportion of the patients. The clinical impact of genomic signatures on local therapy decisions for early breast cancer has been remarkably modest due to the lack of high-level evidence supporting their clinical validity for assessment of the risk of local recurrence. Efforts to personalise breast cancer care must be supported by high level evidence to enable balanced, informed treatment decisions. These considerations underpin the importance of ongoing biomarker-directed clinical trials to generate the high-level evidence necessary for setting the future standard of care in personalised local therapy for patients with early breast cancer.

Multiplexed RNA-FISH-guided Laser Capture Microdissection RNA Sequencing Improves Breast Cancer Molecular Subtyping, Prognostic Classification, and Predicts Response to Antibody Drug Conjugates

On a retrospective cohort of 1,082 FFPE breast tumors, we demonstrated the analytical validity of a test using multiplexed RNA-FISH-guided laser capture microdissection (LCM) coupled with RNA-sequencing (mFISHseq), which showed 93% accuracy compared to immunohistochemistry. The combination of these technologies makes strides in i) precisely assessing tumor heterogeneity, ii) obtaining pure tumor samples using LCM to ensure accurate biomarker expression and multigene testing, and iii) providing thorough and granular data from whole transcriptome profiling. We also constructed a 293-gene intrinsic subtype classifier that performed equivalent to the research based PAM50 and AIMS classifiers. By combining three molecular classifiers for consensus subtyping, mFISHseq alleviated single sample discordance, provided near perfect concordance with other classifiers (κ > 0.85), and reclassified 30% of samples into different subtypes with prognostic implications. We also use a consensus approach to combine information from 4 multigene prognostic classifiers and clinical risk to characterize high, low, and ultra-low risk patients that relapse early (< 5 years), late (> 10 years), and rarely, respectively. Lastly, to identify potential patient subpopulations that may be responsive to treatments like antibody drug-conjugates (ADC), we curated a list of 92 genes and 110 gene signatures to interrogate their association with molecular subtype and overall survival. Many genes and gene signatures related to ADC processing (e.g., antigen/payload targets, endocytosis, and lysosome activity) were independent predictors of overall survival in multivariate Cox regression models, thus highlighting potential ADC treatment-responsive subgroups. To test this hypothesis, we constructed a unique 19-feature classifier using multivariate logistic regression with elastic net that predicted response to trastuzumab emtansine (T-DM1; AUC = 0.96) better than either ERBB2 mRNA or Her2 IHC alone in the T-DM1 arm of the I-SPY2 trial. This test was deployed in a research-use only format on 26 patients and revealed clinical insights into patient selection for novel therapies like ADCs and immunotherapies and de-escalation of adjuvant chemotherapy.

Which Patients Need Chemotherapy? From Pathological Risk Factors to Gene Signatures and Evaluation of Endocrine Response

Background

Chemotherapy, used either before or after surgery, has significantly improved survival in early breast cancer. Accurate risk assessment is essential to avoid both overtreatment and undertreatment. This review provides an overview of the evolution of chemotherapy as well as risk factors for tailored systemic therapies in early breast cancer - from pathologic risk factors to gene expression signatures to endocrine response assessment.

Summary

Chemotherapy has improved dramatically in recent decades from its beginnings with conventionally dosed cyclophosphamide plus methotexate plus 5-fluorouracil to dose-dense anthracycline- and taxane-containing regimens. Similarly, risk assessment has evolved starting from traditional pathologic risk factors such as tumor size, axillary nodal status, and grading. In recent decades, gene expression signatures have improved prognostic accuracy with a high level of evidence. In turn, these signatures can be further improved by incorporating the aforementioned pathologic factors. As an important step away from this static assessment, dynamic assessment of proliferation factor Ki-67 after short-term preoperative endocrine treatment has gained interest to improve risk assessment in early hormone receptor-positive breast cancer.

Key Message

This review highlights advances in chemotherapy and risk assessment in early breast cancer, from pathologic risk factors for recurrence to gene expression signatures and endocrine response assessment. These developments are leading to better risk stratification and thus better adaptation of therapies.

Future Practices of Breast Pathology Using Digital and Computational Pathology

Pathology clinical practice has evolved by adopting technological advancements initially regarded as potentially disruptive, such as electron microscopy, immunohistochemistry, and genomic sequencing. Breast pathology has a critical role as a medical domain, where the patient's pathology diagnosis has significant implications for prognostication and treatment of diseases. The advent of digital and computational pathology has brought about significant advancements in the field, offering new possibilities for enhancing diagnostic accuracy and improving patient care. Digital slide scanning enables to conversion of glass slides into high-fidelity digital images, supporting the review of cases in a digital workflow. Digitization offers the capability to render specimen diagnoses, digital archival of patient specimens, collaboration, and telepathology. Integration of image analysis and machine learning-based systems layered atop the high-resolution digital images offers novel workflows to assist breast pathologists in their clinical, educational, and research endeavors. Decision support tools may improve the detection and classification of breast lesions and the quantification of immunohistochemical studies. Computational biomarkers may help to contribute to patient management or outcomes. Furthermore, using digital and computational pathology may increase standardization and quality assurance, especially in areas with high interobserver variability. This review explores the current landscape and possible future applications of digital and computational techniques in the field of breast pathology.

Perturbation and stability of PAM50 subtyping in population-based primary invasive breast cancer

PAM50 gene expression subtypes represent a cornerstone in the molecular classification of breast cancer and are included in risk prediction models to guide therapy. We aimed to illustrate the impact of included genes and biological processes on subtyping while considering a tumor's underlying clinical subgroup defined by ER, PR, and HER2 status. To do this we used a population-representative and clinically annotated early-stage breast tumor cohort of 6233 samples profiled by RNA sequencing and applied a perturbation strategy of excluding co-expressed genes (gene sets). We demonstrate how PAM50 nearest-centroid classification depends on biological processes present across, but also within, ER/PR/HER2 subgroups and PAM50 subtypes themselves. Our analysis highlights several key aspects of PAM50 classification. Firstly, we demonstrate the tight connection between a tumor's nearest and second-nearest PAM50 centroid. Additionally, we show that the second-best subtype is associated with overall survival in ER-positive, HER2-negative, and node-negative disease. We also note that ERBB2 expression has little impact on PAM50 classification in HER2-positive disease regardless of ER status and that the Basal subtype is highly stable in contrast to the Normal subtype. Improved consciousness of the commonly used PAM50 subtyping scheme will aid in our understanding and interpretation of breast tumors that have seemingly conflicting PAM50 classification when compared to clinical biomarkers. Finally, our study adds further support in challenging the common misconception that PAM50 subtypes are distinct classes by illustrating that PAM50 subtypes in tumors represent a continuum with prognostic implications.

The Prediction Analysis of Microarray 50 (PAM50) Gene Expression Classifier Utilized in Indeterminate-Risk Breast Cancer Patients in Hungary: A Consecutive 5-Year Experience

BACKGROUND

Breast cancer has been categorized into molecular subtypes using immunohistochemical staining (IHC) and fluorescence in situ hybridization (FISH) since the early 2000s. However, recent research suggests that gene expression testing, specifically Prosigna® Prediction Analysis of Microarray 50 (PAM50), provides more accurate classification methods. In this retrospective study, we compared the results of IHC/FISH and PAM50 testing. We also examined the impact of various PAM50 parameters on overall survival (OS) and progression-free survival (PFS).

RESULTS

We analyzed 42 unilateral breast cancer samples, with 18 classified as luminal A, 10 as luminal B, 8 as Human epidermal growth factor receptor 2 (HER2)-positive, and 6 as basal-like using PAM50. Interestingly, 17 out of the 42 samples (40.47%) showed discordant results between histopathological assessment and the PAM50 classifier. While routine IHC/FISH resulted in classification differences for a quarter to a third of samples within each subtype, all basal-like tumors were misclassified. Hormone receptor-positive tumors (hazard rate: 8.7803; p = 0.0085) and patients who had higher 10-year recurrence risk scores (hazard rate: 1.0539; p = 0.0201) had shorter OS and PFS.

CONCLUSIONS

Our study supports the existing understanding of molecular subtypes in breast cancer and emphasizes the overlap between clinical characteristics and molecular subtyping. These findings underscore the value of gene expression profiling, such as PAM50, in improving treatment decisions for breast cancer patients.

Delineating intra-tumoral heterogeneity and tumor evolution in breast cancer using precision-based approaches

The burden of breast cancer continues to increase worldwide as it remains the most diagnosed tumor in females and the second leading cause of cancer-related deaths. Breast cancer is a heterogeneous disease characterized by different subtypes which are driven by aberrations in key genes such as BRCA1 and BRCA2, and hormone receptors. However, even within each subtype, heterogeneity that is driven by underlying evolutionary mechanisms is suggested to underlie poor response to therapy, variance in disease progression, recurrence, and relapse. Intratumoral heterogeneity highlights that the evolvability of tumor cells depends on interactions with cells of the tumor microenvironment. The complexity of the tumor microenvironment is being unraveled by recent advances in screening technologies such as high throughput sequencing; however, there remain challenges that impede the practical use of these approaches, considering the underlying biology of the tumor microenvironment and the impact of selective pressures on the evolvability of tumor cells. In this review, we will highlight the advances made thus far in defining the molecular heterogeneity in breast cancer and the implications thereof in diagnosis, the design and application of targeted therapies for improved clinical outcomes. We describe the different precision-based approaches to diagnosis and treatment and their prospects. We further propose that effective cancer diagnosis and treatment are dependent on unpacking the tumor microenvironment and its role in driving intratumoral heterogeneity. Underwriting such heterogeneity are Darwinian concepts of natural selection that we suggest need to be taken into account to ensure evolutionarily informed therapeutic decisions.

Clinical Treatment Score Post-5 Years as a Tool for Risk Estimation of Late Recurrence in Thai Patients With Estrogen-Receptor-Positive, Early Breast Cancer: A Validation Study

Background

The risk of late distant recurrence (LDR) of estrogen receptor (ER)-positive breast cancer continues even after 5 years of endocrine treatment. Clinical Treatment Score after 5 years (CTS5) was developed and validated as a tool to assess the risk of LDR using data from Tamoxifen, Arimidex Alone or in Combinations (ATAC) and Breast International Group 1-98 (BIG1-98) trials. This study aimed to externally validate CTS5 in a real-world cohort of patients treated at an academic center in Thailand.

Methods

The study was a retrospective analytical research study of early-stage, ER-positive breast cancer patients. The primary endpoint was LDR. The risk of LDR was determined using the CTS5 calculator. Cox regression model and Kaplan-Meier survival analysis were applied for prognostic validation of CTS5. Calibration was performed by comparing observed LDR to expected LDR using the Hosmer-Lemeshow (H-L) test.

Results

A total of 323 women were included with a median follow-up period of 11.6 years. The rate of LDR was 10.8%. The CTS5 was prognostic for LDR. C-index of the area under the ROC curve was 0.672. There was no significant difference between actual and expected numbers of LDR with an observed (O) LDR events to expected (E) number of LDR events ratio of 0.99 (0.86-1.12) (H-L P = .79) indicating a proper calibration in this cohort.

Conclusions

Our study validated that CTS5 is accurate in predicting the risk of LDR in ER-positive breast cancer cases in Thai patients. Its performance seemed to be better in postmenopausal patients. CTS5 could be applied in routine clinical practice to improve decisions regarding prolonged endocrine therapy, particularly in resource-limited countries where molecular profiling are inaccessible.

PAM50 intrinsic subtypes, risk of recurrence score and breast cancer survival in HIV-positive and HIV-negative patients-a South African cohort study

PURPOSE

Treatment decision making for patients with breast cancer increasingly depends on analysis of markers or systems for estimating risk of breast cancer recurrence. Breast cancer intrinsic subtypes and risk of recurrence (ROR) scores have been found to be valuable in predicting survival and determining optimal treatment for individual patients. We studied the association of breast cancer survival with the PAM50 gene expression assay in HIV-positive and HIV-negative patients.

METHOD

RNA was extracted from formalin-fixed paraffin-embedded specimens of histologically confirmed invasive carcinoma and was purified using the AllPrep® DNA/RNA FFPE kit, Qiagen (Hilden, Germany). The NanoString RUO PAM50 algorithm was used to determine the molecular subtype and the risk of recurrence score of each sample. The overall and disease-free survival were determined with comparison made among HIV-positive and -negative patients. We then generated Kaplan-Meier survival curves, calculated p-values and estimated hazard ratios and their 95% confidence intervals using Cox regression models.

RESULTS

Of the 384 RNA samples analysed, 98.4% met the required RNA quality standard and the specified QC threshold for the test. Luminal B was the most common PAM50 intrinsic subtype and 82.1% of patients were at high risk for disease recurrence based on ROR score. HIV infection, PAM50-based HER2-enriched and basal-like intrinsic subtypes, and high ROR were associated with poor overall and disease-free survival. HIV-positive patients with luminal A & B subtypes had significantly worse survival outcomes than HIV-negative luminal patents.

CONCLUSION

Aggressive tumour biology was common in our cohort. HIV infection, PAM50 HER2-enriched,basal-like intrinsic subtypes and high ROR score were associated with poor overall and disease-free survival. HIV infection impacted survival in patients with luminal subtypes only.

New Biomarkers and Treatment Advances in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is a specific subtype of breast cancer lacking hormone receptor expression and HER2 gene amplification. TNBC represents a heterogeneous subtype of breast cancer, characterized by poor prognosis, high invasiveness, high metastatic potential, and a tendency to relapse. In this review, the specific molecular subtypes and pathological aspects of triple-negative breast cancer are illustrated, with particular attention to the biomarker characteristics of TNBC, namely: regulators of cell proliferation and migration and angiogenesis, apoptosis-regulating proteins, regulators of DNA damage response, immune checkpoints, and epigenetic modifications. This paper also focuses on omics approaches to exploring TNBC, such as genomics to identify cancer-specific mutations, epigenomics to identify altered epigenetic landscapes in cancer cells, and transcriptomics to explore differential mRNA and protein expression. Moreover, updated neoadjuvant treatments for TNBC are also mentioned, underlining the role of immunotherapy and novel and targeted agents in the treatment of TNBC.

The Chorioallantoic Membrane Xenograft Assay as a Reliable Model for Investigating the Biology of Breast Cancer

The chorioallantoic membrane (CAM) assay is an alternative in vivo model that allows for minimally invasive research of cancer biology. Using the CAM assay, we investigated phenotypical and functional characteristics (tumor grade, mitosis rate, tumor budding, hormone receptor (HR) and HER2 status, Ki-67 proliferation index) of two breast cancer cell lines, MCF-7 and MDA-MB-231, which resemble the HR+ (luminal) and triple-negative breast cancer (TNBC) subgroups, respectively. Moreover, the CAM results were directly compared with murine MCF-7- and MDA-MB-231-derived xenografts and human patient TNBC tissue. Known phenotypical and biological features of the aggressive triple-negative breast cancer cell line (MDA-MB-231) were confirmed in the CAM assay, and mouse xenografts. Furthermore, the histomorphological and immunohistochemical variables assessed in the CAM model were similar to those in human patient tumor tissue. Given the confirmation of the classical biological and growth properties of breast cancer cell lines in the CAM model, we suggest this in vivo model to be a reliable alternative test system for breast cancer research to reduce murine animal experiments.

Discordance between PAM50 intrinsic subtyping and immunohistochemistry in South African women with breast cancer

PURPOSE

Breast cancer is a heterogeneous disease with different gene expression profiles, treatment options and outcomes. In South Africa, tumors are classified using immunohistochemistry. In high-income countries multiparameter genomic assays are being utilized with implications for tumor classification and treatment.

METHODS

In a cohort of 378 breast cancer patients from the SABCHO study, we investigated the concordance between tumor samples classified by IHC and the PAM50 gene assay.

RESULTS

IHC classified patients as ER-positive (77.5%), PR-positive (70.6%), and HER2-positive (32.3%). These results, together with Ki67, were used as surrogates for intrinsic subtyping, and showed 6.9% IHC-A-clinical, 72.7% IHC-B-clinical, 5.3% IHC-HER2-clinical and 15.1% triple negative cancer (TNC). Typing using the PAM50 gave 19.3% luminal-A, 32.5% luminal-B, 23.5% HER2-enriched and 24.6% basal-like. The basal-like and TNC had the highest concordance, while the luminal-A and IHC-A group had the lowest concordance. By altering the cutoff for Ki67, and realigning the HER2/ER/PR-positive patients to IHC-HER2, we improved concordance with the intrinsic subtypes.

CONCLUSION

We suggest that the Ki67 be changed to a cutoff of 20-25% in our population to better reflect the luminal subtype classifications. This change would inform treatment options for breast cancer patients in settings where genomic assays are unaffordable.

Adjuvant paclitaxel and trastuzumab for node-negative, HER2-positive breast cancer: final 10-year analysis of the open-label, single-arm, phase 2 APT trial

BACKGROUND

We aimed to report on long-term outcomes of patients with small, node-negative, HER2-positive breast cancer treated with adjuvant paclitaxel and trastuzumab and to establish potential biomarkers to predict prognosis.

METHODS

In this open-label, single-arm, phase 2 study, patients aged 18 years or older, with small (≤3 cm), node-negative, HER2-positive breast cancer, and an Eastern Cooperative Oncology Group performance status of 0-1, were recruited from 16 institutions in 13 cities in the USA. Eligible patients were given intravenous paclitaxel (80 mg/m²) with intravenous trastuzumab (loading dose of 4 mg/kg, subsequent doses 2 mg/kg) weekly for 12 weeks, followed by trastuzumab (weekly at 2 mg/kg or once every 3 weeks at 6 mg/kg) for 40 weeks to complete a full year of trastuzumab. The primary endpoint was 3-year invasive disease-free survival. Here, we report 10-year survival outcomes, assessed in all participants who received protocol-defined treatment, with exploratory analyses using the HER2DX genomic tool. This study is registered on ClinicalTrials.gov, NCT00542451, and is closed to accrual.

FINDINGS

Between Oct 29, 2007, and Sept 3, 2010, 410 patients were enrolled and 406 were given adjuvant paclitaxel and trastuzumab and included in the analysis. Mean age at enrolment was 55 years (SD 10·5), 405 (99·8%) of 406 patients were female and one (0·2%) was male, 350 (86·2%) were White, 28 (6·9%) were Black or African American, and 272 (67·0%) had hormone receptor-positive disease. After a median follow-up of 10·8 years (IQR 7·1-11·4), among 406 patients included in the analysis population, we observed 31 invasive disease-free survival events, of which six (19·4%) were locoregional ipsilateral recurrences, nine (29·0%) were new contralateral breast cancers, six (19·4%) were distant recurrences, and ten (32·3%) were all-cause deaths. 10-year invasive disease-free survival was 91·3% (95% CI 88·3-94·4), 10-year recurrence-free interval was 96·3% (95% CI 94·3-98·3), 10-year overall survival was 94·3% (95% CI 91·8-96·8), and 10-year breast cancer-specific survival was 98·8% (95% CI 97·6-100). HER2DX risk score as a continuous variable was significantly associated with invasive disease-free survival (hazard ratio [HR] per 10-unit increment 1·24 [95% CI 1·00-1·52]; p=0·047) and recurrence-free interval (1·45 [1·09-1·93]; p=0·011).

INTERPRETATION

Adjuvant paclitaxel and trastuzumab is a reasonable treatment standard for patients with small, node-negative, HER2-positive breast cancer. The HER2DX genomic tool might help to refine the prognosis for this population.

FUNDING

Genentech.

Blood-Based mRNA Tests as Emerging Diagnostic Tools for Personalised Medicine in Breast Cancer

Molecular diagnostic tests help clinicians understand the underlying biological mechanisms of their patients' breast cancer (BC) and facilitate clinical management. Several tissue-based mRNA tests are used routinely in clinical practice, particularly for assessing the BC recurrence risk, which can guide treatment decisions. However, blood-based mRNA assays have only recently started to emerge. This review explores the commercially available blood mRNA diagnostic assays for BC. These tests enable differentiation of BC from non-BC subjects (Syantra DX, BCtect), detection of small tumours <10 mm (early BC detection) (Syantra DX), detection of different cancers (including BC) from a single blood sample (multi-cancer blood test Aristotle), detection of BC in premenopausal and postmenopausal women and those with high breast density (Syantra DX), and improvement of diagnostic outcomes of DNA testing (variant interpretation) (+RNAinsight). The review also evaluates ongoing transcriptomic research on exciting possibilities for future assays, including blood transcriptome analyses aimed at differentiating lymph node positive and negative BC, distinguishing BC and benign breast disease, detecting ductal carcinoma in situ, and improving early detection further (expression changes can be detected in blood up to eight years before diagnosing BC using conventional approaches, while future metastatic and non-metastatic BC can be distinguished two years before BC diagnosis).

Triple-Negative PAM50 Non-Basal Breast Cancer Subtype Predicts Benefit from Extended Adjuvant Capecitabine

PURPOSE

Predictive biomarkers for capecitabine benefit in triple-negative breast cancer (TNBC) have been recently proposed using samples from phase III clinical trials, including non-basal phenotype and biomarkers related to angiogenesis, stroma, and capecitabine activation genes. We aimed to validate these findings on the larger phase III GEICAM/CIBOMA clinical trial.

EXPERIMENTAL DESIGN

Tumor tissues from patients with TNBC randomized to standard (neo)adjuvant chemotherapy followed by capecitabine versus observation were analyzed using a 164-gene NanoString custom nCounter codeset measuring mRNA expression. A prespecified statistical plan sought to verify the predictive capacity of PAM50 non-basal molecular subtype and tested the hypotheses that breast tumors with increased expression of (meta)genes for cytotoxic cells, mast cells, endothelial cells, PDL2, and 38 individual genes benefit from adjuvant capecitabine for distant recurrence-free survival (DRFS; primary endpoint) and overall survival.

RESULTS

Of the 876 women enrolled in the GEICAM/CIBOMA trial, 658 (75%) were evaluable for analysis (337 with capecitabine and 321 without). Of these cases, 553 (84%) were profiled as PAM50 basal-like whereas 105 (16%) were PAM50 non-basal. Non-basal subtype was the most significant predictor for capecitabine benefit [HRcapecitabine, 0.19; 95% confidence interval (CI), 0.07-0.54; P < 0.001] when compared with PAM50 basal-like (HRcapecitabine, 0.9; 95% CI, 0.63-1.28; P = 0.55; Pinteraction<0.001, adjusted P value = 0.01). Analysis of biological processes related to PAM50 non-basal subtype revealed its enrichment for mast cells, extracellular matrix, angiogenesis, and features of mesenchymal stem-like TNBC subtype.

CONCLUSIONS

In this prespecified correlative analysis of the GEICAM/CIBOMA trial, PAM50 non-basal status identified patients with early-stage TNBC most likely to benefit from capecitabine.

Clinical implications of the intrinsic molecular subtypes in hormone receptor-positive and HER2-negative metastatic breast cancer

Traditionally, the classification of breast cancer relies on the expression of immunohistochemical (IHC) biomarkers readily available in clinical practice. Using highly standardized and reproducible assays across patient cohorts, intrinsic molecular subtypes of breast cancer - also called "intrinsic subtypes" (IS) - have been identified based on the expression of 50 genes. Although IHC-based subgroups and IS moderately correlate to each other, they are not superimposable. In fact, non-luminal biology has been detected in a substantial proportion (5-20%) of hormone receptor-positive (HoR+) tumors, has prognostic value, and identifies reduced and increased sensitivity to endocrine therapy and chemotherapy, respectively. During tumor progression, a shift toward a non-luminal estrogen-independent and more aggressive phenotype has been demonstrated. Intrinsic genomic instability and cell plasticity, alone or combined with external constraints deriving from treatment selective pressure or interplay with the tumor microenvironment, may represent the determinants of such biological diversity between primary and metastatic disease, and during metastatic tumor evolution. In this review, we describe the distribution and the clinical behavior of IS as the disease progresses, focusing on HoR+/HER2-negative advanced breast cancer. In addition, we provide an overview of the ongoing clinical trials aiming to validate the predictive and prognostic value of IS towards their incorporation into routine care.

Extended Adjuvant Endocrine Treatment in Luminal Breast Cancers in the Era of Genomic Tests

In patients with early-stage endocrine receptor-positive (ER+) breast cancer (BC), adjuvant endocrine therapy (ET) for 5 years is the standard of care. However, for some patients, the risk of recurrence remain high for up to 15 years after diagnosis and extended ET beyond 5 years may be a reasonable option. Nevertheless, this strategy significantly increases the occurrence of side effects. Here we summarize the available evidence from randomized clinical trials on the efficacy and safety profile of extended ET and discuss available clinical and genomic tools helpful to select eligible patients in daily clinical practice.

Transcriptomic profiling of Indian breast cancer patients revealed subtype-specific mRNA and lncRNA signatures

Breast cancer (BC) is one of the leading causes of cancer-associated death in women. Despite the progress in therapeutic regimen, resistance and recurrence of breast cancer have affected the overall survival of patients. The present signatures, such as PAM50 and Oncotype DX, do not segregate the Indian breast samples based on molecular subtypes. This study aims at finding signatures of long noncoding RNA (lncRNA) and mRNA in Indian breast cancer patients using RNA-seq. We have analyzed the survival based on the menopausal and hormone status of 380 Indian breast cancer patients, and of these, we have sequenced and analyzed matched tumor–normal transcriptome of 17 (pre- and postmenopausal) Indian breast cancer patients representing six different subtypes, namely, four patients in triple-positive, three patients in estrogen receptor–positive (ER+ve), three patients in estrogen and progesterone receptors–positive (ER+ve, PR+ve), two patients in human epidermal growth factor receptor (Her2+ve), three patients in triple-negative, and one patient in ER+ve and Her2+ve subtypes. We have identified a 25 mRNA–27 lncRNA gene set, which segregated the subtypes in our data. A pathway analysis of the differentially expressed genes revealed downregulated ECM interaction and upregulated immune regulation, cell cycle, DNA damage response and repair, and telomere elongation in premenopausal women. Postmenopausal women showed downregulated metabolism, innate immune system, upregulated translation, sumoylation, and AKT2 activation. A Kaplan–Meier survival analysis revealed that menopausal status, grade of the tumor, and hormonal status displayed statistically significant effects (p < 0.05) on the risk of mortality due to breast cancer. Her2+ve patients showed low overall survival. One of the unique lncRNA-mRNA pairs specific to the EP-subtype, SNHG12 and EPB41, showed interaction, which correlates with their expression level; SNHG12 is downregulated and EPB41 is upregulated in EP samples.

SOLTI-1904 ACROPOLI TRIAL: efficacy of spartalizumab monotherapy across tumor-types expressing high levels of PD1 mRNA

Improved selection of cancer patients who are most likely to respond to immune checkpoint inhibitors remains an unmet clinical need. Recently, a positive correlation between levels of PD1 mRNA and clinical outcome in response to PD1 blockade across diverse tumor histologies has been confirmed in several datasets. ACROPOLI is a parallel cohort, non-randomized, phase II study that aims to evaluate the efficacy of the anti-PD1 immune checkpoint inhibitor spartalizumab as monotherapy in metastatic patients with solid tumors that express high levels of PD1 (cohort 1; n = 111). An additional cohort of 30 patients with tumors expressing low levels of PD1, where PD1/PD-L1 antibodies in monotherapy are standard treatment, will also be included (cohort 2). Primary end point is overall response rate in cohort 1. Trial registration number: NCT04802876 (ClinicalTrials.gov).

Transcriptional Profile Associated with Clinical Outcomes in Metastatic Hormone-Sensitive Prostate Cancer Treated with Androgen Deprivation and Docetaxel

(1) Background: Androgen deprivation therapy (ADT) and docetaxel (DX) combination is a standard therapy for metastatic hormone-sensitive prostate cancer (mHSPC) patients. (2) Methods: We investigate if tumor transcriptomic analysis predicts mHSPC evolution in a multicenter retrospective biomarker study. A customized panel of 184 genes was tested in mRNA from tumor samples by the nCounter platform in 125 mHSPC patients treated with ADT+DX. Gene expression was correlated with castration-resistant prostate cancer-free survival (CRPC-FS) and overall survival (OS). (3) Results: High expression of androgen receptor (AR) signature was independently associated with longer CRPC-FS (hazard ratio (HR) 0.6, 95% confidence interval (CI) 0.3–0.9; p = 0.015), high expression of estrogen receptor (ESR) signature with longer CRPC-FS (HR 0.6, 95% CI 0.4–0.9; p = 0.019) and OS (HR 0.5, 95% CI 0.2–0.9, p = 0.024), and lower expression of tumor suppressor genes (TSG) (RB1, PTEN and TP53) with shorter OS (HR 2, 95% CI 1–3.8; p = 0.044). ARV7 expression was independently associated with shorter CRPC-FS (HR 1.5, 95% CI 1.1–2.1, p = 0.008) and OS (HR 1.8, 95% CI 1.2–2.6, p = 0.004), high ESR2 was associated with longer OS (HR 0.5, 95% CI 0.2–1, p = 0.048) and low expression of RB1 was independently associated with shorter OS (HR 1.9, 95% CI 1.1–3.2, p = 0.014). (4) Conclusions: AR, ESR, and TSG expression signatures, as well as ARV7, RB1, and ESR2 expression, have a prognostic value in mHSPC patients treated with ADT+DX.

RNA sequencing-based single sample predictors of molecular subtype and risk of recurrence for clinical assessment of early-stage breast cancer

Multigene assays for molecular subtypes and biomarkers can aid management of early invasive breast cancer. Using RNA-sequencing we aimed to develop single-sample predictor (SSP) models for clinical markers, subtypes, and risk of recurrence (ROR). A cohort of 7743 patients was divided into training and test set. We trained SSPs for subtypes and ROR assigned by nearest-centroid (NC) methods and SSPs for biomarkers from histopathology. Classifications were compared with Prosigna in two external cohorts (ABiM, n = 100 and OSLO2-EMIT0, n = 103). Prognostic value was assessed using distant recurrence-free interval. Agreement between SSP and NC for PAM50 (five subtypes) was high (85%, Kappa = 0.78) for Subtype (four subtypes) very high (90%, Kappa = 0.84) and for ROR risk category high (84%, Kappa = 0.75, weighted Kappa = 0.90). Prognostic value was assessed as equivalent and clinically relevant. Agreement with histopathology was very high or high for receptor status, while moderate for Ki67 status and poor for Nottingham histological grade. SSP and Prosigna concordance was high for subtype (OSLO-EMIT0 83%, Kappa = 0.73 and ABiM 80%, Kappa = 0.72) and moderate and high for ROR risk category (68 and 84%, Kappa = 0.50 and 0.70, weighted Kappa = 0.70 and 0.78). Pooled concordance for emulated treatment recommendation dichotomized for chemotherapy was high (85%, Kappa = 0.66). Retrospective evaluation suggested that SSP application could change chemotherapy recommendations for up to 17% of postmenopausal ER+/HER2-/N0 patients with balanced escalation and de-escalation. Results suggest that NC and SSP models are interchangeable on a group-level and nearly so on a patient level and that SSP models can be derived to closely match clinical tests.

The current staging and classification systems of breast cancer and their pitfalls: Is it possible to integrate the complexity of this neoplasm into a unified staging system?

Breast cancer is one of the leading causes of cancer death in women worldwide due to its variable aggressiveness and high propensity to develop distant metastases. The staging can be performed clinically or pathologically, generating the stage stratification by the TNM (T - tumor size; N- lymph node metastasis; M - distant organ metastasis) system. However, cancers with virtually identical TNM characteristics can present highly contrasting behaviors due to the divergence of molecular profiles. This review focuses on the histopathological nuances and molecular understanding of breast cancer through the profiling of gene and protein expression, culminating in improvements promoted by the integration of this information into the traditional staging system. As a culminating point, it will highlight predictive statistical tools for genomic risks and decision algorithms as a possible solution to integrate the various systems because they have the potential to reduce the indications for such tests, serving as a funnel in association with staging and previous classification.

Identification of genomic determinants contributing to cytokine release in immunotherapies and human diseases

BACKGROUND

Cytokine release syndrome (CRS) is a strong immune system response that can occur as a result of the reaction of a cellular immunotherapy with malignant cells. While the frequency and management of CRS in CAR T-cell therapy has been well documented, there is emerging interest in pre-emptive treatment to reduce CRS severity and improve overall outcomes. Accordingly, identification of genomic determinants that contribute to cytokine release may lead to the development of targeted therapies to prevent or abrogate the severity of CRS.

METHODS

Forty three clinical CD22 CAR T-cell products were collected for RNA extraction. 100 ng of mRNA was used for Nanostring assay analysis which is based on the nCounter platform. Several public datasets were used for validation purposes.

RESULTS

We found the expression of the PFKFB4 gene and glycolytic pathway activity were upregulated in CD22 CAR T-cells given to patients who developed CRS compared to those who did not experience CRS. Moreover, these results were further validated in cohorts with COVID-19, influenza infections and autoimmune diseases, and in tumor tissues. The findings were similar, except that glycolytic pathway activity was not increased in patients with influenza infections and systemic lupus erythematosus (SLE).

CONCLUSION

Our data strongly suggests that PFKFB4 acts as a driving factor in mediating cytokine release in vivo by regulating glycolytic activity. Our results suggest that it would beneficial to develop drugs targeting PFKFB4 and the glycolytic pathway for the treatment of CRS.

Relationship of micro-RNA, mRNA and eIF Expression in Tamoxifen-Adapted MCF-7 Breast Cancer Cells: Impact of miR-1972 on Gene Expression, Proliferation and Migration

Background: Tamoxifen-adapted MCF-7-Tam cells represent an in-vitro model for acquired tamoxifen resistance, which is still a problem in clinics. We here investigated the correlation of microRNA-, mRNA- and eukaryotic initiation factors (eIFs) expression in this model. Methods: MicroRNA- and gene expression were analyzed by nCounter and qRT-PCR technology; eIFs by Western blotting. Protein translation mode was determined using a reporter gene assay. Cells were transfected with a miR-1972-mimic. Results: miR-181b-5p,-3p and miR-455-5p were up-, miR-375, and miR-1972 down-regulated and are significant in survival analysis. About 5% of the predicted target genes were significantly altered. Pathway enrichment analysis suggested a contribution of the FoxO1 pathway. The ratio of polio-IRES driven to cap-dependent protein translation shifted towards cap-dependent initiation. Protein expression of eIF2A, -4G, -4H and -6 decreased, whereas eIF3H was higher in MCF-7-Tam. Significant correlations between tamoxifen-regulated miRNAs and eIFs were found in representative breast cancer cell lines. Transfection with a miR-1972-mimic reverses tamoxifen-induced expression for a subset of genes and increased proliferation in MCF-7, but reduced proliferation in MCF-7-Tam, especially in the presence of 4OH-tamoxifen. Migration was inhibited in MCF-7-Tam cells. Translation mode remained unaffected. Conclusions: miR-1972 contributes to the orchestration of gene-expression and physiological consequences of tamoxifen adaption.

The Tumor-Specific Expression of L1 Retrotransposons Independently Correlates with Time to Relapse in Hormone-Negative Breast Cancer Patients

BACKGROUND

Long-Interspersed Nuclear Element (L1) retrotransposons are silenced in healthy tissues but unrepressed in cancer. Even if L1 reactivation has been associated with reduced overall survival in breast cancer (BC) patients, a comprehensive correlation with clinicopathological features is still missing.

METHODS

Using quantitative, reverse-transcription PCR, we assessed L1 mRNA expression in 12 BC cells, 210 BC patients and in 47 normal mammary tissues. L1 expression was then correlated with molecular and clinicopathological data.

RESULTS

We identified a tumor-exclusive expression of L1s, absent in normal mammary cells and tissues. A positive correlation between L1 expression and tumor dedifferentiation, lymph-node involvement and increased immune infiltration was detected. Molecular subtyping highlighted an enrichment of L1s in basal-like cells and cancers. By exploring disease-free survival, we identified L1 overexpression as an independent biomarker for patients with a high risk of recurrence in hormone-receptor-negative BCs.

CONCLUSIONS

Overall, L1 reactivation identified BCs with aggressive features and patients with a worse clinical fate.

Are we closer to being able to select patients with node-positive hormone receptor-positive breast cancer who can safely omit chemotherapy?

The treatment of hormone receptor-positive, HER2-negative breast cancer has become increasingly individualized, thanks to the development of genomic testing. Gene expression assays provide clinicians and patients with both prognostic and predictive information regarding breast cancer recurrence risk and potential benefit of chemotherapy. While the ability to tailor therapy based on clinicopathologic and genomic factors has enabled a growing number of women to forego chemotherapy, several questions remain regarding how best to apply genomic assay results across varying subgroups of women. Here, we review the role of genomic assays for patients with both lymph node-negative and lymph node-positive breast cancer, and how these assays may help us more precisely select patients with hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2−) breast cancer with or without lymph node involvement who can safely omit chemotherapy in the future.

The ability to classify patients based on gene-expression data varies by algorithm and performance metric

By classifying patients into subgroups, clinicians can provide more effective care than using a uniform approach for all patients. Such subgroups might include patients with a particular disease subtype, patients with a good (or poor) prognosis, or patients most (or least) likely to respond to a particular therapy. Transcriptomic measurements reflect the downstream effects of genomic and epigenomic variations. However, high-throughput technologies generate thousands of measurements per patient, and complex dependencies exist among genes, so it may be infeasible to classify patients using traditional statistical models. Machine-learning classification algorithms can help with this problem. However, hundreds of classification algorithms exist-and most support diverse hyperparameters-so it is difficult for researchers to know which are optimal for gene-expression biomarkers. We performed a benchmark comparison, applying 52 classification algorithms to 50 gene-expression datasets (143 class variables). We evaluated algorithms that represent diverse machine-learning methodologies and have been implemented in general-purpose, open-source, machine-learning libraries. When available, we combined clinical predictors with gene-expression data. Additionally, we evaluated the effects of performing hyperparameter optimization and feature selection using nested cross validation. Kernel- and ensemble-based algorithms consistently outperformed other types of classification algorithms; however, even the top-performing algorithms performed poorly in some cases. Hyperparameter optimization and feature selection typically improved predictive performance, and univariate feature-selection algorithms typically outperformed more sophisticated methods. Together, our findings illustrate that algorithm performance varies considerably when other factors are held constant and thus that algorithm selection is a critical step in biomarker studies.

RCRdiff: A fully integrated Bayesian method for differential expression analysis using raw NanoString nCounter data

The medium-throughput mRNA abundance platform NanoString nCounter has gained great popularity in the past decade, due to its high sensitivity and technical reproducibility as well as remarkable applicability to ubiquitous formalin fixed paraffin embedded (FFPE) tissue samples. Based on RCRnorm developed for normalizing NanoString nCounter data and Bayesian LASSO for variable selection, we propose a fully integrated Bayesian method, called RCRdiff, to detect differentially expressed (DE) genes between different groups of tissue samples (eg, normal and cancer). Unlike existing methods that often require normalization performed beforehand, RCRdiff directly handles raw read counts and jointly models the behaviors of different types of internal controls along with DE and non-DE gene patterns. Doing so would avoid efficiency loss caused by ignoring estimation uncertainty from the normalization step in a sequential approach and thus can offer more reliable statistical inference. We also propose clustering-based strategies for DE gene selection, which do not require any external dataset and are free of any arbitrary cutoff. Empirical evidence of the attractiveness of RCRdiff is demonstrated via extensive simulation and data examples.

BRCA1 Variant Assessment Using a Simple Analytic Assay

BACKGROUND

We previously developed a biological assay to accurately predict BRCA1 (BRCA1 DNA repair associated) mutation status, based on gene expression profiles of Epstein-Barr virus-transformed lymphoblastoid cell lines. The original work was done using whole genome expression microarrays, and nearest shrunken centroids analysis. While these approaches are appropriate for model building, they are difficult to implement clinically, where more targeted testing and analysis are required for time and cost savings.

METHODS

Here, we describe adaptation of the original predictor to use the NanoString nCounter platform for testing, with analysis based on the k-top scoring pairs (k-TSP) method.

RESULTS

Assessing gene expression using the nCounter platform on a set of lymphoblastoid cell lines yielded 93.8% agreement with the microarray-derived data, and 87.5% overall correct classification of BRCA1 carriers and controls. Using the original gene expression microarray data used to develop our predictor with nearest shrunken centroids, we rebuilt a classifier based on the k-TSP method. This classifier relies on the relative expression of 10 pairs of genes, compared to the original 43 identified by nearest shrunken centroids (NSC), and was 96.2% concordant with the original training set prediction, with a 94.3% overall correct classification of BRCA1 carriers and controls.

CONCLUSIONS

The k-TSP classifier was shown to accurately predict BRCA1 status using data generated on the nCounter platform and is feasible for initiating a clinical validation.

The Clinical Research and Latest Application of Genomic Assays in Early-Stage Breast Cancer

Breast cancer is a kind of malignant tumor that seriously endangers women's life and health. Once diagnosed, most patients will receive a combination of treatments to achieve a cure. However, breast cancer is a heterogeneous disease. Even with the same clinical stage and pathological features, its response to treatment and postoperative recurrence risk may still be completely different. With the advent of genomic assay, some patients with early-stage breast cancer who originally needed treatment can still achieve long-term disease-free survival without adjuvant chemotherapy, so as to achieve personalized and accurate treatment mode to a certain extent. In this paper, we reviewed the 5 most widely used and studied genomic panel technologies in breast cancer, namely Oncotype DX, MammaPrint, RecurIndex, PAM50, and EndoPredict, according to accessibility and availability. Based on the results of the completed or ongoing clinical studies, we summarized the origin, applicable population, and clinical efficacy of each detection method, and discussed the potential development prospect of detection technology in the future.

Prognostic and therapeutic significance of COP9 signalosome subunit CSN5 in prostate cancer

Chromosome 8q gain is associated with poor clinical outcomes in prostate cancer, but the underlying biological mechanisms remain to be clarified. CSN5, a putative androgen receptor (AR) partner that is located on chromosome 8q, is the key subunit of the COP9 signalosome, which deactivates ubiquitin ligases. Deregulation of CSN5 could affect diverse cellular functions that contribute to tumor development, but there has been no comprehensive study of its function in prostate cancer. The clinical significance of CSN5 amplification/overexpression was evaluated in 16 prostate cancer clinical cohorts. Its oncogenic activity was assessed by genetic and pharmacologic perturbations of CSN5 activity in prostate cancer cell lines. The molecular mechanisms of CSN5 function were assessed, as was the efficacy of the CSN5 inhibitor CSN5i-3 in vitro and in vivo. Finally, the transcription cofactor activity of CSN5 in prostate cancer cells was determined. The prognostic significance of CSN5 amplification and overexpression in prostate cancer was independent of MYC amplification. Inhibition of CSN5 inhibited its oncogenic function by targeting AR signaling, DNA repair, multiple oncogenic pathways, and spliceosome regulation. Furthermore, inhibition of CSN5 repressed metabolic pathways, including oxidative phosphorylation and glycolysis in AR-negative prostate cancer cells. Targeting CSN5 with CSN5i-3 showed potent antitumor activity in vitro and in vivo. Importantly, CSN5i-3 synergizes with PARP inhibitors to inhibit prostate cancer cell growth. CSN5 functions as a transcription cofactor to cooperate with multiple transcription factors in prostate cancer. Inhibiting CSN5 strongly attenuates prostate cancer progression and could enhance PARP inhibition efficacy in the treatment of prostate cancer.

Biomarker discovery studies for patient stratification using machine learning analysis of omics data: a scoping review

OBJECTIVE

To review biomarker discovery studies using omics data for patient stratification which led to clinically validated FDA-cleared tests or laboratory developed tests, in order to identify common characteristics and derive recommendations for future biomarker projects.

DESIGN

Scoping review.

METHODS

We searched PubMed, EMBASE and Web of Science to obtain a comprehensive list of articles from the biomedical literature published between January 2000 and July 2021, describing clinically validated biomarker signatures for patient stratification, derived using statistical learning approaches. All documents were screened to retain only peer-reviewed research articles, review articles or opinion articles, covering supervised and unsupervised machine learning applications for omics-based patient stratification. Two reviewers independently confirmed the eligibility. Disagreements were solved by consensus. We focused the final analysis on omics-based biomarkers which achieved the highest level of validation, that is, clinical approval of the developed molecular signature as a laboratory developed test or FDA approved tests.

RESULTS

Overall, 352 articles fulfilled the eligibility criteria. The analysis of validated biomarker signatures identified multiple common methodological and practical features that may explain the successful test development and guide future biomarker projects. These include study design choices to ensure sufficient statistical power for model building and external testing, suitable combinations of non-targeted and targeted measurement technologies, the integration of prior biological knowledge, strict filtering and inclusion/exclusion criteria, and the adequacy of statistical and machine learning methods for discovery and validation.

CONCLUSIONS

While most clinically validated biomarker models derived from omics data have been developed for personalised oncology, first applications for non-cancer diseases show the potential of multivariate omics biomarker design for other complex disorders. Distinctive characteristics of prior success stories, such as early filtering and robust discovery approaches, continuous improvements in assay design and experimental measurement technology, and rigorous multicohort validation approaches, enable the derivation of specific recommendations for future studies.

Sample Preparation Approach Influences PAM50 Risk of Recurrence Score in Early Breast Cancer

The PAM50 gene expression subtypes and the associated risk of recurrence (ROR) score are used to predict the risk of recurrence and the benefits of adjuvant therapy in early-stage breast cancer. The Prosigna assay includes the PAM50 subtypes along with their clinicopathological features, and is approved for treatment recommendations for adjuvant hormonal therapy and chemotherapy in hormone-receptor-positive early breast cancer. The Prosigna test utilizes RNA extracted from macrodissected tumor cells obtained from formalin-fixed, paraffin-embedded (FFPE) tissue sections. However, RNA extracted from fresh-frozen (FF) bulk tissue without macrodissection is widely used for research purposes, and yields high-quality RNA for downstream analyses. To investigate the impact of the sample preparation approach on ROR scores, we analyzed 94 breast carcinomas included in an observational study that had available gene expression data from macrodissected FFPE tissue and FF bulk tumor tissue, along with the clinically approved Prosigna scores for the node-negative, hormone-receptor-positive, HER2-negative cases (n = 54). ROR scores were calculated in R; the resulting two sets of scores from FFPE and FF samples were compared, and treatment recommendations were evaluated. Overall, ROR scores calculated based on the macrodissected FFPE tissue were consistent with the Prosigna scores. However, analyses from bulk tissue yielded a higher proportion of cases classified as normal-like; these were samples with relatively low tumor cellularity, leading to lower ROR scores. When comparing ROR scores (low, intermediate, and high), discordant cases between the two preparation approaches were revealed among the luminal tumors; the recommended treatment would have changed in a minority of cases.

Neoadjuvant eribulin in HER2-negative early-stage breast cancer (SOLTI-1007-NeoEribulin): a multicenter, two-cohort, non-randomized phase II trial

Eribulin prolongs overall survival in patients with pre-treated advanced breast cancer. However, no biomarker exists to prospectively select patients who will benefit the most from this drug. SOLTI-1007-NeoEribulin is a phase II, open-label, two-cohort, exploratory pharmacogenomic study in patients with clinical stage I–II HER2-negative breast cancer receiving neoadjuvant eribulin monotherapy treatment. Primary objective was to explore the association of baseline tumor gene expression with pathological complete response in the breast (pCR_B) at surgery. Key secondary objectives were pCR_B rates in all patients and according to HR status, gene expression changes during treatment and safety. One-hundred one hormonal receptor-positive (HR + ) and seventy-three triple-negative breast cancer (TNBC) patients were recruited. The pCR_B rates were 6.4% in all patients, 4.9% in HR + disease and 8.2% in TNBC. The TNBC cohort was interrupted due to a progression disease rate of 30.1%. The pCR_B rates differed according to intrinsic subtypes: 28.6% in HER2-enriched, 11.1% in Normal-like, 7.9% in Luminal B, 5.9% in Basal-like and 0% in Luminal A (HER2-enriched vs. others odds ratio = 7.05, 95% CI 1.80–42.14; p-value = 0.032). Intrinsic subtype changes at surgery occurred in 33.3% of cases, mostly (49.0%) Luminal B converting to Luminal A or Basal-like converting to Normal-like. Baseline tumor-infiltrating lymphocytes (TILs) were significantly associated with pCR. Eribulin showed a good safety profile with a low response and pCR_B rates. Patients with HER2-negative disease with a HER2-enriched profile may benefit the most from eribulin. In addition, significant biological activity of eribulin is observed in Luminal B and Basal-like subtypes.

Nanostring-Based Identification of the Gene Expression Profile in Trigger Finger Samples

Trigger finger is a common yet vastly understudied fibroproliferative hand pathology, severely affecting patients' quality of life. Consistent trauma due to inadequate positioning within the afflicted finger's tendon/pulley system leads to cellular dysregulation and eventual fibrosis. While the genetic characteristics of the fibrotic tissue in the trigger finger have been studied, the pathways that govern the initiation and propagation of fibrosis are still unknown. The complete gene expression profile of the trigger finger has never been explored. Our study has used the Nanostring nCounter gene expression assay to investigate the molecular signaling involved in trigger finger pathogenesis. We collected samples from patients undergoing trigger finger (n = 4) release surgery and compared the gene expression to carpal tunnel tissue (n = 4). Nanostring nCounter analysis identified 165 genes that were differentially regulated; 145 of these genes were upregulated, whereas 20 genes were downregulated. We found that several collagen genes were significantly upregulated, and a regulatory matrix metalloproteinase (MMP), MMP-3, was downregulated. Bioinformatic analysis revealed that several known signaling pathways were dysregulated, such as the TGF-β1 and Wnt signaling pathways. We also found several novel signaling pathways (e.g., PI3K, MAPK, JAK-STAT, and Notch) differentially regulated in trigger finger. The outcome of our study helps in understanding the molecular signaling pathway involved in the pathogenesis of the trigger finger.

Analytical validation of a multi-biomarker algorithmic test for prediction of progressive kidney function decline in patients with early-stage kidney disease

Background

The KidneyIntelX™ test applies a machine learning algorithm that incorporates plasma biomarkers and clinical variables to produce a composite risk score to predict a progressive decline in kidney function in patients with type 2 diabetes (T2D) and early-stage chronic kidney disease (CKD). The following studies describe the analytical validation of the KidneyIntelX assay including impact of observed methodologic variability on the composite risk score.

Methods

Analytical performance studies of sensitivity, precision, and linearity were performed on three biomarkers assayed in multiplexed format: kidney injury molecule-1 (KIM-1), soluble tumor necrosis factor receptor-1 (sTNFR-1) and soluble tumor necrosis factor receptor-2 (sTNFR-2) based on Clinical Laboratory Standards Institute (CLSI) guidelines. Analytical variability across twenty (20) experiments across multiple days, operators, and reagent lots was assessed to examine the impact on the reproducibility of the composite risk score. Analysis of cross-reactivity and interfering substances was also performed.

Results

Assays for KIM-1, sTNFR-1 and sTNFR-2 demonstrated acceptable sensitivity. Mean within-laboratory imprecision coefficient of variation (CV) was established as less than 9% across all assays in a multi-lot study. The linear range of the assays was determined as 12–5807 pg/mL, 969–23,806 pg/mL and 4256–68,087 pg/mL for KIM-1, sTNFR-1 and sTNFR-2, respectively. The average risk score CV% was less than 5%, with 98% concordance observed for assignment of risk categories. Cross-reactivity between critical assay components in a multiplexed format did not exceed 1.1%.

Conclusions

The set of analytical validation studies demonstrated robust analytical performance across all three biomarkers contributing to the KidneyIntelX risk score, meeting or exceeding specifications established during characterization studies. Notably, reproducibility of the composite risk score demonstrated that expected analytical laboratory variation did not impact the assigned risk category, and therefore, the clinical validity of the reported results.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12014-021-09332-y.

What Do We Have to Know about PD-L1 Expression in Prostate Cancer? A Systematic Literature Review. Part 3: PD-L1, Intracellular Signaling Pathways and Tumor Microenvironment

The tumor microenvironment (TME) includes immune (T, B, NK, dendritic), stromal, mesenchymal, endothelial, adipocytic cells, extracellular matrix, and cytokines/chemokines/soluble factors regulating various intracellular signaling pathways (ISP) in tumor cells. TME influences the survival/progression of prostate cancer (PC), enabling tumor cell immune-evasion also through the activation of the PD-1/PD-L1 axis. We have performed a systematic literature review according to the PRISMA guidelines, to investigate how the PD-1/PD-L1 pathway is influenced by TME and ISPs. Tumor immune-escape mechanisms include suppression/exhaustion of tumor infiltrating cytotoxic T lymphocytes, inhibition of tumor suppressive NK cells, increase in immune-suppressive immune cells (regulatory T, M2 macrophagic, myeloid-derived suppressor, dendritic, stromal, and adipocytic cells). IFN-γ (the most investigated factor), TGF-β, TNF-α, IL-6, IL-17, IL-15, IL-27, complement factor C5a, and other soluble molecules secreted by TME components (and sometimes increased in patients' serum), as well as and hypoxia, influenced the regulation of PD-L1. Experimental studies using human and mouse PC cell lines (derived from either androgen-sensitive or androgen-resistant tumors) revealed that the intracellular ERK/MEK, Akt-mTOR, NF-kB, WNT and JAK/STAT pathways were involved in PD-L1 upregulation in PC. Blocking the PD-1/PD-L1 signaling by using immunotherapy drugs can prevent tumor immune-escape, increasing the anti-tumor activity of immune cells.

Are There Limits in Explainability of Prognostic Biomarkers? Scrutinizing Biological Utility of Established Signatures

Prognostic biomarkers can have an important role in the clinical practice because they allow stratification of patients in terms of predicting the outcome of a disorder. Obstacles for developing such markers include lack of robustness when using different data sets and limited concordance among similar signatures. In this paper, we highlight a new problem that relates to the biological meaning of already established prognostic gene expression signatures. Specifically, it is commonly assumed that prognostic markers provide sensible biological information and molecular explanations about the underlying disorder. However, recent studies on prognostic biomarkers investigating 80 established signatures of breast and prostate cancer demonstrated that this is not the case. We will show that this surprising result is related to the distinction between causal models and predictive models and the obfuscating usage of these models in the biomedical literature. Furthermore, we suggest a falsification procedure for studies aiming to establish a prognostic signature to safeguard against false expectations with respect to biological utility.

Impact of molecular subtype and race on HR+, HER2- breast cancer survival

PURPOSE

There is an urgent need to understand the biological factors contributing to the racial survival disparity among women with hormone receptor-positive (HR+), HER2- breast cancer. In this study, we examined the impact of PAM50 subtype on 10-year mortality rate in women with HR+, HER2- breast cancer by race.

METHODS

Women with localized, HR+, HER2- breast cancer diagnosed between 2002 and 2012 from two population-based cohorts were evaluated. Archival tumors were obtained and classified by PAM50 into four molecular subtypes (i.e., luminal A, luminal B, HER2-enriched, and basal-like). The molecular subtypes within HR+, HER2- breast cancers and corresponding 10-year mortality rate were compared between Black and Non-Hispanic White (NHW) women using Cox proportional hazard ratios and survival analysis, adjusting for covariates.

RESULTS

In this study, 318 women with localized, HR+, HER2- breast cancer were included-227 Black (71%) and 91 NHW (29%). Young Black women (age ≤ 50) had the highest proportion of HR+, non-luminal A tumors (47%), compared to young NHW (10%), older Black women (31%), and older NHW (30%). Overall, women with HR+, non-luminal A subtypes had a higher 10-year mortality rate compared to HR+, luminal A subtypes after adjustment for age, stage, and income (HR 4.21 for Blacks, 95% CI 1.74-10.18 and HR 3.44 for NHW, 95% CI 1.31-9.03). Among HR+, non-luminal A subtypes there was, however, no significant racial difference in 10-yr mortality observed (Black vs. NHW: HR 1.23, 95% CI 0.58-2.58).

CONCLUSION

Molecular subtype classification highlights racial disparities in PAM50 subtype distribution among women with HR+, HER2- breast cancer. Among women with HR+, HER2- breast cancer, racial survival disparities are ameliorated after adjusting for molecular subtype.