Development and verification of the PAM50-based Prosigna breast cancer gene signature assay

Navigating precision: the crucial role of next-generation sequencing recurrence risk assessment in tailoring adjuvant therapy for hormone receptor-positive, human epidermal growth factor Receptor2-negative early breast cancer

Hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) breast cancer is the most common subtype, representing over two-thirds of new diagnoses. Adjuvant therapy, which encompasses various medications and treatment durations, is the standard approach for managing early stage HR+ HER2- breast cancer. Optimizing treatment is essential to minimize unnecessary side effects while addressing the biological variability inherent in HR+/HER2- breast cancers. Incorporating biological biomarkers into treatment decisions, alongside traditional clinical factors, is vital. Gene expression assays can identify patients unlikely to benefit from adjuvant chemotherapy, thereby refining treatment strategies and improving risk assessment. This paper reviews evidence for several genomic tests, including Oncotype DX, MammaPrint, Breast Cancer Index, RucurIndex, and EndoPredict, which assist in tailoring adjuvant therapy. Additionally, we explore the role of liquid biopsies in personalizing treatment, emphasizing the importance of considering late relapse risks and potential benefits of extended systemic therapy for HR+/HER2- breast cancer patients.

Updates in Molecular Profiling of Pancreatic Ductal Adenocarcinoma

Outcomes from pancreatic ductal adenocarcinoma (PDAC) remain poor and better methods of prognostication and therapeutic approaches are needed. Recent advances in cancer genomics have led to the development of molecular subtypes of PDAC associated with clinical outcomes. Current evidence also suggests that the subtypes have differential response to first-line chemotherapy regimens. PDAC is also characterized by different stroma and immune environments. Further work is needed to confirm the utility of these subtypes to predicting response to different systemic therapies.

scPanel: a tool for automatic identification of sparse gene panels for generalizable patient classification using scRNA-seq datasets

Single-cell RNA sequencing (scRNA-seq) technologies can generate transcriptomic profiles at a single-cell resolution in large patient cohorts, facilitating discovery of gene and cellular biomarkers for disease. Yet, when the number of biomarker genes is large, the translation to clinical applications is challenging due to prohibitive sequencing costs. Here, we introduce scPanel, a computational framework designed to bridge the gap between biomarker discovery and clinical application by identifying a sparse gene panel for patient classification from the cell population(s) most responsive to perturbations (e.g. diseases/drugs). scPanel incorporates a data-driven way to automatically determine a minimal number of informative biomarker genes. Patient-level classification is achieved by aggregating the prediction probabilities of cells associated with a patient using the area under the curve score. Application of scPanel to scleroderma, colorectal cancer, and COVID-19 datasets resulted in high patient classification accuracy using only a small number of genes (<20), automatically selected from the entire transcriptome. In the COVID-19 case study, we demonstrated cross-dataset generalizability in predicting disease state in an external patient cohort. scPanel outperforms other state-of-the-art gene selection methods for patient classification and can be used to identify parsimonious sets of reliable biomarker candidates for clinical translation.

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.

Elevated expression of Aurora-A/AURKA in breast cancer associates with younger age and aggressive features

BACKGROUND AND OBJECTIVE

Aurora kinase A (AURKA) is reported to be overexpressed in breast cancer. In addition to its role in regulating cell cycle and mitosis, studies have reported AURKA involvements in oncogenic signaling in suppressing BRCA1 and BRCA2. We aimed to characterize AURKA protein and mRNA expression in a breast cancer cohort of the young, investigating its relation to clinico-pathologic features and survival, and exploring age-related AURKA-associated biological processes.

METHODS

Aurora kinase A immunohistochemical staining was performed on tissue microarrays of primary tumors from an in-house breast cancer cohort (n = 355) with information on clinico-pathologic data, molecular markers, and long and complete follow-up. A subset of the in-house cohort (n = 127) was studied by the NanoString Breast Cancer 360 expression panel for exploration of mRNA expression. METABRIC cohorts < 50 years at breast cancer diagnosis (n = 368) were investigated for differentially expressed genes and enriched gene sets in AURKA mRNA high tumors stratified by age. Differentially expressed genes and gene sets were investigated using network analyses and g:Profiler.

RESULTS

High Aurora kinase A protein expression associated with aggressive clinico-pathologic features, a basal-like subtype, and high risk of recurrence score. These patterns were confirmed using mRNA data. High AURKA gene expression demonstrated independent prognostic value when adjusted for traditional clinico-pathologic features and molecular subtypes. Notably, high AURKA expression significantly associated with reduced disease-specific survival within patients below 50 years, also within the luminal A subtype. Tumors of high AURKA expression showed gene expression patterns reflecting increased DNA damage activation and higher BRCAness score.

CONCLUSIONS

Our findings indicate higher AURKA expression in young breast cancer, and associations between high Aurora-A/AURKA and aggressive tumor features, including higher tumor cell proliferation, and shorter survival, in the young. Our findings point to AURKA as a marker for increased DNA damage and DNA repair deficiency and suggest AURKA as a biomarker of clinical relevance in young breast cancer.

Obtention of viable cell suspensions from breast cancer tumor biopsies for 3D chromatin conformation and single-cell transcriptome analysis

Molecular and cellular characterization of tumors is essential due to the complex and heterogeneous nature of cancer. In recent decades, many bioinformatic tools and experimental techniques have been developed to achieve personalized characterization of tumors. However, sample handling continues to be a major challenge as limitations such as prior treatments before sample acquisition, the amount of tissue obtained, transportation, or the inability to process fresh samples pose a hurdle for experimental strategies that require viable cell suspensions. Here, we present an optimized protocol that allows the recovery of highly viable cell suspensions from breast cancer primary tumor biopsies. Using these cell suspensions we have successfully characterized genome architecture through Hi-C. Also, we have evaluated single-cell gene expression and the tumor cellular microenvironment through single-cell RNAseq. Both technologies are key in the detailed and personalized molecular characterization of tumor samples. The protocol described here is a cost-effective alternative to obtain viable cell suspensions from biopsies simply and efficiently.

Validation of an AI-based solution for breast cancer risk stratification using routine digital histopathology images

BACKGROUND

Stratipath Breast is a CE-IVD marked artificial intelligence-based solution for prognostic risk stratification of breast cancer patients into high- and low-risk groups, using haematoxylin and eosin (H&E)-stained histopathology whole slide images (WSIs). In this validation study, we assessed the prognostic performance of Stratipath Breast in two independent breast cancer cohorts.

METHODS

This retrospective multi-site validation study included 2719 patients with primary breast cancer from two Swedish hospitals. The Stratipath Breast tool was applied to stratify patients based on digitised WSIs of the diagnostic H&E-stained tissue sections from surgically resected tumours. The prognostic performance was evaluated using time-to-event analysis by multivariable Cox Proportional Hazards analysis with progression-free survival (PFS) as the primary endpoint.

RESULTS

In the clinically relevant oestrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative patient subgroup, the estimated hazard ratio (HR) associated with PFS between low- and high-risk groups was 2.76 (95% CI: 1.63-4.66, p-value < 0.001) after adjusting for established risk factors. In the ER+/HER2- Nottingham histological grade (NHG) 2 subgroup, the HR was 2.20 (95% CI: 1.22-3.98, p-value = 0.009) between low- and high-risk groups.

CONCLUSION

The results indicate an independent prognostic value of Stratipath Breast among all breast cancer patients, as well as in the clinically relevant ER+/HER2- subgroup and the NHG2/ER+/HER2- subgroup. Improved risk stratification of intermediate-risk ER+/HER2- breast cancers provides information relevant for treatment decisions of adjuvant chemotherapy and has the potential to reduce both under- and overtreatment. Image-based risk stratification provides the added benefit of short lead times and substantially lower cost compared to molecular diagnostics and therefore has the potential to reach broader patient groups.

Evaluating the Prognostic Role of the PAM50 Signature and Selected Immune-Related Signatures for Recurrence in Patients With T1abN0 Breast Cancer

BACKGROUND

De-escalation of adjuvant treatment in patients with T1abN0 breast cancer is discussed internationally. Identification of new prognostic factors in these patients may assist this de-escalation. The PAM50 signature and tumor inflammation signature (TIS), Programmed Cell Death Protein 1 (PD-1) and Programmed Cell Death Ligand 1 (PD-L1) signatures are possible prognostic factors for recurrence.

MATERIALS AND METHODS

Danish patients with T1abN0 breast cancer diagnosed between 2007-2016 were identified, the NanoString Breast Cancer 360 Panel was performed on tissue samples from cases with recurrence matched 1:1 with controls without recurrence (n = 234). The association between gene signatures and recurrence was analyzed with conditional logistic regression.

RESULTS

Patients with the basal-like subtype had higher values of TIS, PD-1 and PD-L1 scores compared with other subtypes. Patients with higher PD-L1 score had significantly lower odds of recurrence (odds ratio [OR] 0.61, P = .01). Likewise, an increased TIS score was associated to lower, but nonsignificant odds of recurrence (OR 0.76, P = .07). Patients with human epidermal growth factor receptor 2 (HER2)-enriched subtype had significantly higher odds of recurrence compared with patients with luminal A subtype (OR 4.8, P = .03).

DISCUSSION

PAM50 and immune-related signatures provide important prognostic information in patients with T1abN0 breast cancer, which may refine the risk assessment in these patients.

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.

The Expression Profiles of lncRNAs Are Associated with Neoadjuvant Chemotherapy Resistance in Locally Advanced, Luminal B-Type Breast Cancer

lncRNAs are noncoding transcripts with tissue and cancer specificity. Particularly, in breast cancer, lncRNAs exhibit subtype-specific expression; they are particularly upregulated in luminal tumors. However, no gene signature-based laboratory tests have been developed for luminal breast cancer identification or the differential diagnosis of luminal tumors, since no luminal A- or B-specific genes have been identified. Particularly, luminal B patients are of clinical interest, since they have the most variable response to neoadjuvant treatment; thus, it is necessary to develop diagnostic and predictive biomarkers for these patients to optimize treatment decision-making and improve treatment quality. In this study, we analyzed the lncRNA expression profiles of breast cancer cell lines and patient tumor samples from RNA-Seq data to identify an lncRNA signature specific for luminal phenotypes. We identified an lncRNA signature consisting of LINC01016, GATA3-AS1, MAPT-IT1, and DSCAM-AS1 that exhibits luminal subtype-specific expression; among these lncRNAs, GATA3-AS1 is associated with the presence of residual disease (Wilcoxon test, p < 0.05), which is related to neoadjuvant chemotherapy resistance in luminal B breast cancer patients. Furthermore, analysis of GATA3-AS1 expression using RNA in situ hybridization (RNA ISH) demonstrated that this lncRNA is detectable in histological slides. Similar to estrogen receptors and Ki67, both commonly detected biomarkers, GATA3-AS1 proves to be a suitable predictive biomarker for clinical application in breast cancer laboratory tests.

Genomics in Clinical trials for Breast Cancer

Breast cancer (B.C.) still has increasing incidences and mortality rates globally. It is known that B.C. and other cancers have a very high rate of genetic heterogeneity and genomic mutations. Traditional oncology approaches have not been able to provide a lasting solution. Targeted therapeutics have been instrumental in handling the complexity and resistance associated with B.C. However, the progress of genomic technology has transformed our understanding of the genetic landscape of breast cancer, opening new avenues for improved anti-cancer therapeutics. Genomics is critical in developing tailored therapeutics and identifying patients most benefit from these treatments. The next generation of breast cancer clinical trials has incorporated next-generation sequencing technologies into the process, and we have seen benefits. These innovations have led to the approval of better-targeted therapies for patients with breast cancer. Genomics has a role to play in clinical trials, including genomic tests that have been approved, patient selection and prediction of therapeutic response. Multiple clinical trials in breast cancer have been done and are still ongoing, which have applied genomics technology. Precision medicine can be achieved in breast cancer therapy with increased efforts and advanced genomic studies in this domain. Genomics studies assist with patient outcomes improvement and oncology advancement by providing a deeper understanding of the biology behind breast cancer. This article will examine the present state of genomics in breast cancer clinical trials.

High Caveolin-1 mRNA expression in triple-negative breast cancer is associated with an aggressive tumor microenvironment, chemoresistance, and poor clinical outcome

BACKGROUND

Currently, there are few treatment-predictive and prognostic biomarkers in triple-negative breast cancer (TNBC). Caveolin-1 (CAV1) is linked to chemoresistance and several important processes involved in tumor progression and metastasis, such as epithelial-mesenchymal transition (EMT). Herein, we report that high CAV1 gene expression is an independent factor of poor prognosis in TNBC.

METHODS

CAV1 gene expression was compared across different molecular features (e.g., PAM50 subtypes). CAV1 expression was assessed in relation to clinical outcomes using Cox regression adjusted for clinicopathological predictors. Differential gene expression and gene set enrichment analyses were applied to compare high- and low-expressing CAV1 tumors. Tumor microenvironment composition of high- and low-expressing CAV1 tumors was estimated using ECOTYPER. Tumor tissue microarrays were used to evaluate CAV1 protein levels in stromal and malignant cells.

RESULTS

In the SCAN-B (n = 525) and GSE31519 (n = 327) cohorts, patients with CAV1-high tumors had an increased incidence of early recurrence adjusted HR 1.78 (95% CI 1.12-2.81) and 2.20 (95% CI 1.39-3.47), respectively. In further analysis, high CAV1 gene expression was associated with a molecular profile indicating altered metabolism, neovascularization, chemoresistance, EMT, suppressed immune response, and active tumor microenvironment. Protein levels of CAV1 in malignant and stromal cells were not correlated with CAV1 gene expression.

CONCLUSION

CAV1 gene expression in TNBC is a biomarker that merits further investigation in clinical trials and as a therapeutic target.

Differences in 21-Gene and PAM50 Recurrence Scores in Younger and Black Women With Breast Cancer

PURPOSE

Genomic tests, such as the Oncotype Dx 21-gene and Prosigna risk of recurrence (ROR-P) assay, are commonly used for breast cancer prognostication. Emerging data suggest variability between assays, but this has not been compared in diverse populations.

MATERIALS AND METHODS

RNA sequencing was performed on 647 previously untreated stage I-III estrogen receptor-positive/human epidermal growth factor receptor 2-negative tumors in the Carolina Breast Cancer Study, which oversampled Black and younger women (age <50 years at diagnosis), using research versions of two common RNA-based prognostic assays: ROR-PR and the 21-gene recurrence score (RSR). Relative frequency differences and 95% CIs were estimated for associations with race and age, and hazards of 5-year local or distant recurrence were modeled with Cox regression. Proliferation and estrogen module scores from each assay, representing broad activity of genes in those pathways, were examined to guide interpretation of differences between tests.

RESULTS

Among both younger and older individuals, Black women had higher frequency of intermediate and high ROR-PR scores than non-Black women. Race was not significantly associated with RSR in either age group. High (hazard ratio [HR], 4.67 [95% CI, 1.73 to 12.70]) and intermediate (HR, 2.12 [95% CI, 0.98 to 4.62]) ROR-PR scores were associated with greater risk of recurrence, but RSR did not predict recurrence. RSR emphasized estrogen over proliferation modules, whereas ROR-PR emphasized proliferation. Higher proliferation scores were associated with younger age and Black race in both assays. Modifications to the RSR algorithm that increased emphasis on proliferation improved prognostication in this diverse population.

CONCLUSION

ROR-PR and the 21-gene RSR differentially emphasize estrogen-related and proliferative biology. The emphasis of 21-gene RS on estrogen-related biology and lower endocrine therapy initiation among Black women may contribute to poorer prognostic ability in heterogeneously treated populations.

Effect of cross-platform gene-expression, computational methods on breast cancer subtyping in PALOMA-2 and PALLET studies

Intrinsic breast cancer molecular subtyping (IBCMS) provides significant prognostic information for patients with breast cancer and helps determine treatment. This study compared IBCMS methods on various gene-expression platforms in PALOMA-2 and PALLET trials. PALOMA-2 tumor samples were profiled using EdgeSeq and nanostring and subtyped with AIMS, PAM50, and research-use-only (ruo)Prosigna. PALLET tumor biopsies were profiled using mRNA sequencing and subtyped with AIMS and PAM50. In PALOMA-2 (n = 222), a 54% agreement was observed between results from AIMS and gold-standard ruoProsigna, with AIMS assigning 67% basal-like to HER2-enriched. In PALLET (n = 224), a 69% agreement was observed between results from PAM50 and AIMS. Different IBCMS methods may lead to different results and could misguide treatment selection; hence, a standardized clinical PAM50 assay and computational approach should be used.Trial number: NCT01740427.

Association of early menarche with breast tumor molecular features and recurrence

BACKGROUND

Early menarche is an established risk factor for breast cancer but its molecular contribution to tumor biology and prognosis remains unclear.

METHODS

We profiled transcriptome-wide gene expression in breast tumors (N = 846) and tumor-adjacent normal tissues (N = 666) from women in the Nurses' Health Studies (NHS) to investigate whether early menarche (age < 12) is associated with tumor molecular and prognostic features in women with breast cancer. Multivariable linear regression and pathway analyses using competitive gene set enrichment analysis were conducted in both tumor and adjacent-normal tissue and externally validated in TCGA (N = 116). Subgroup analyses stratified on ER-status based on the tumor were also performed. PAM50 signatures were used for tumor molecular subtyping and to generate proliferation and risk of recurrence scores. We created a gene expression score using LASSO regression to capture early menarche based on 28 genes from FDR-significant pathways in breast tumor tissue in NHS and tested its association with 10-year disease-free survival in both NHS (N = 836) and METABRIC (N = 952).

RESULTS

Early menarche was significantly associated with 369 individual genes in adjacent-normal tissues implicated in extracellular matrix, cell adhesion, and invasion (FDR ≤ 0.1). Early menarche was associated with upregulation of cancer hallmark pathways (18 significant pathways in tumor, 23 in tumor-adjacent normal, FDR ≤ 0.1) related to proliferation (e.g. Myc, PI3K/AKT/mTOR, cell cycle), oxidative stress (e.g. oxidative phosphorylation, unfolded protein response), and inflammation (e.g. pro-inflammatory cytokines IFN α and IFN γ ). Replication in TCGA confirmed these trends. Early menarche was associated with significantly higher PAM50 proliferation scores (β = 0.082 [0.02-0.14]), odds of aggressive molecular tumor subtypes (basal-like, OR = 1.84 [1.18-2.85] and HER2-enriched, OR = 2.32 [1.46-3.69]), and PAM50 risk of recurrence score (β = 4.81 [1.71-7.92]). Our NHS-derived early menarche gene expression signature was significantly associated with worse 10-year disease-free survival in METABRIC (N = 952, HR = 1.58 [1.10-2.25]).

CONCLUSIONS

Early menarche is associated with more aggressive molecular tumor characteristics and its gene expression signature within tumors is associated with worse 10-year disease-free survival among women with breast cancer. As the age of onset of menarche continues to decline, understanding its relationship to breast tumor characteristics and prognosis may lead to novel secondary prevention strategies.

The VEGF-Hypoxia Signature Is Upregulated in Basal-like Breast Tumors from Women of African Ancestry and Associated with Poor Outcomes in Breast Cancer

PURPOSE

Black women experience the highest breast cancer mortality rate compared with women of other racial/ethnic groups. To gain a deeper understanding of breast cancer heterogeneity across diverse populations, we examined a VEGF-hypoxia gene expression signature in breast tumors from women of diverse ancestry.

EXPERIMENTAL DESIGN

We developed a NanoString nCounter gene expression panel and applied it to breast tumors from Nigeria (n = 182) and the University of Chicago (Chicago, IL; n = 161). We also analyzed RNA sequencing data from Nigeria (n = 84) and The Cancer Genome Atlas (TCGA) datasets (n = 863). Patient prognosis was analyzed using multiple datasets.

RESULTS

The VEGF-hypoxia signature was highest in the basal-like subtype compared with other subtypes, with greater expression in Black women compared with White women. In TCGA dataset, necrotic breast tumors had higher scores for the VEGF-hypoxia signature compared with non-necrosis tumors (P < 0.001), with the highest proportion in the basal-like subtype. Furthermore, necrotic breast tumors have higher scores for the proliferation signature, suggesting an interaction between the VEGF-hypoxia signature, proliferation, and necrosis. T-cell gene expression signatures also correlated with the VEGF-hypoxia signature when testing all tumors in TCGA dataset. Finally, we found a significant association of the VEGF-hypoxia profile with poor outcomes when using all patients in the METABRIC (P < 0.0001) and SCAN-B datasets (P = 0.002).

CONCLUSIONS

These data provide further evidence for breast cancer heterogeneity across diverse populations and molecular subtypes. Interventions selectively targeting VEGF-hypoxia and the immune microenvironment have the potential to improve overall survival in aggressive breast cancers that disproportionately impact Black women in the African Diaspora.

Breast Cancer Histopathology in the Age of Molecular Oncology

For more than a century, microscopic histology has been the cornerstone for cancer diagnosis, and breast carcinoma is no exception. In recent years, clinical biomarkers, gene expression profiles, and other molecular tests have shown increasing utility for identifying the key biological features that guide prognosis and treatment of breast cancer. Indeed, the most common histologic pattern-invasive ductal carcinoma of no special type-provides relatively little guidance to management beyond triggering grading, biomarker testing, and clinical staging. However, many less common histologic patterns can be recognized by trained pathologists, which in many cases can be linked to characteristic biomarker and gene expression patterns, underlying mutations, prognosis, and therapy. Herein we describe more than a dozen such histomorphologic subtypes (including lobular, metaplastic, salivary analog, and several good prognosis special types of breast cancer) in the context of their molecular and clinical features.

Is the CTS5 a helpful decision-making tool in the extended adjuvant therapy setting?

PURPOSE

The Clinical Treatment Score post-5 years (CTS5) is an easy-to-use tool estimating the late distant recurrence (LDR) risk in patients with hormone receptor-positive breast cancer after 5 years of endocrine therapy (ET). Apart from evaluating the prognostic value and calibration accuracy of CTS5, the aim of this study is to clarify if this score is able to identify patients at higher risk for LDR who will benefit from extended ET.

METHODS

Prognostic power, calibration, and predictive value of the CTS5 was tested in patients of the prospective ABCSG-06 and -06a trials (n = 1254 and 860 patients, respectively). Time to LDR was analyzed with Cox regression models.

RESULTS

Higher rates of LDR in the years five to ten were observed in high- and intermediate-risk patients compared to low-risk patients (HR 4.02, 95%CI 2.26-7.15, p < 0.001 and HR 1.93, 95%CI 1.05-3.56, p = 0.035). An increasing continuous CTS5 was associated with increasing LDR risk (HR 2.23, 95% CI 1.74-2.85, p < 0.001). Miscalibration of CTS5 in high-risk patients could be observed. Although not reaching significance, high-risk patients benefitted the most from prolonged ET with an absolute reduction of the estimated 5-year LDR of - 6.1% (95%CI - 14.4 to 2.3).

CONCLUSION

The CTS5 is a reliable prognostic tool that is well calibrated in the lower and intermediate risk groups with a substantial difference of expected versus observed LDR rates in high-risk patients. While a numerical trend in favoring prolonged ET for patients with a higher CTS5 was found, a significantly predictive value for the score could not be confirmed.

CLINICAL TRIAL REGISTRATION

ABCSG-06 trial (NCT00309491), ABCSG-06A7 1033AU/0001 (NCT00300508).

Impact of Prosigna test on adjuvant treatment decision in lymph node-negative early breast cancer-a prospective national multicentre study (EMIT-1)

BACKGROUND

EMIT-1 is a national, observational, single-arm trial designed to assess the value of the Prosigna, Prediction Analysis of Microarray using the 50 gene classifier (PAM50)/Risk of Recurrence (ROR), test as a routine diagnostic tool, examining its impact on adjuvant treatment decisions, clinical outcomes, side-effects and cost-effectiveness. Here we present the impact on treatment decisions.

PATIENTS AND METHODS

Patients with hormone receptor-positive, human epidermal growth factor receptor 2-negative pT1-pT2 lymph node-negative early breast cancer (EBC) were included. The Prosigna test and standard histopathology assessments were carried out. Clinicians' treatment decisions were recorded before (pre-Prosigna) and after (post-Prosigna) the Prosigna test results were disclosed.

RESULTS

Of 2217 patients included, 2178 had conclusive Prosigna results. The pre-Prosigna treatment decisions were: no systemic treatment (NT) in 27% of patients, endocrine treatment alone (ET) in 38% and chemotherapy (CT) followed by ET (CT + ET) in 35%. Post-Prosigna treatment decisions were 25% NT, 51% ET and 24% CT + ET, respectively. Adjuvant treatment changed in 28% of patients, including 21% change in CT use. Among patients assigned to CT + ET pre-Prosigna, 45% were de-escalated to ET post-Prosigna. Of patients assigned to ET, 12% were escalated to CT + ET and 8% were de-escalated to NT; of those assigned to NT, 18% were escalated to ET/CT + ET. CT was more frequently recommended for patients aged ≤50 years. In the subgroup with pT1c-pT2 G2 and intermediate Ki67 (0.5-1.5× local laboratory median Ki67 score), the pre-Prosigna CT treatment decision varied widely across hospitals (3%-51%). Post-Prosigna, the variability of CT use was markedly reduced (8%-24%). The correlation between Ki67 and ROR score within this subgroup was poor (r = 0.25-0.39). The median ROR score increased by increasing histological grade, but the ROR score ranges were wide (for G1 0-79, G2 0-90, G3 16-94).

CONCLUSION

The Prosigna test result changed adjuvant treatment decisions in all EBC clinical risk groups, markedly decreased the CT use for patients categorized as higher clinical risk pre-Prosigna and reduced treatment decision discrepancies between hospitals.

Enhancing clinical decision support with genomic tools in breast cancer: A Scottish perspective

INTRODUCTION

The Oncotype DX Breast RS test has been adopted in Scotland and has been the subject of a large population-based study by a Scottish Consensus Group to assess the uptake of the recurrence score (RS), evaluate co-variates associated with the RS and to analyse the effect it may have had on clinical practice.

MATERIALS & METHODS

Pan-Scotland study between August 2018-August 2021 evaluating 833 patients who had a RS test performed as part of their diagnostic pathway. Data was extracted retrospectively from electronic records and analysis conducted to describe change in chemotherapy administration (by direct comparison with conventional risk assessment tools), and univariate/multivariate analysis to assess relationship between covariates and the RS.

RESULTS

Chemotherapy treatment was strongly influenced by the RS (p < 0.001). Only 30 % of patients received chemotherapy treatment in the intermediate and high risk PREDICT groups, where chemotherapy is considered. Additionally, 55.5 % of patients with a high risk PREDICT had a low RS and did not receive chemotherapy. There were 17 % of patients with a low risk PREDICT but high RS who received chemotherapy. Multivariate regression analysis showed the progesterone receptor Allred score (PR score) to be a strong independent predictor of the RS, with a negative PR score being associated with high RS (OR 4.49, p < 0.001). Increasing grade was also associated with high RS (OR 3.81, p < 0.001). Classic lobular pathology was associated with a low RS in comparison to other tumour pathology (p < 0.01). Nodal disease was associated with a lower RS (p = 0.012) on univariate analysis, with menopausal status (p = 0.43) not influencing the RS on univariate or multivariate analysis.

CONCLUSIONS

Genomic assays offer the potential for risk-stratified decision making regarding the use of chemotherapy. They can help reduce unnecessary chemotherapy treatment and identify a subgroup of patients with more adverse genomic tumour biology. A recent publication by Health Improvement Scotland (HIS) has updated guidance on use of the RS test for NHS Scotland. It suggests to limit its use to the intermediate risk PREDICT group. Our study shows the impact of the RS test in the low and high risk PREDICT groups. The implementation across Scotland has resulted in a notable shift in practice, leading to a significant reduction in chemotherapy administration in the setting of high risk PREDICT scores returning low risk RS. There has also been utility for the test in the low risk PREDICT group to detect a small subgroup with a high RS. We have found the PR score to have a strong independent association with high risk RS. This finding was not evaluated by the key RS test papers, and the potential prognostic information provided by the PR score as a surrogate biomarker is an outstanding question that requires more research to validate.

The Evolving Role of Genomic Testing in Early Breast Cancer: Implications for Diagnosis, Prognosis, and Therapy

Multigene prognostic genomic assays have become indispensable in managing early breast cancer (EBC), offering crucial information for risk stratification and guiding adjuvant treatment strategies in conjunction with traditional clinicopathological parameters. The American Society of Clinical Oncology (ASCO) guidelines endorse these assays, though some clinical contexts still lack definitive recommendations. The dynamic landscape of EBC management demands further refinement and optimization of genomic assays to streamline their incorporation into clinical practice. The breast cancer community is poised at the brink of transformative advances in enhancing the clinical utility of genomic assays, aiming to significantly improve the precision and effectiveness of both diagnosis and treatment for women with EBC. This article methodically examines the testing methodologies, clinical validity and utility, costs, diagnostic frameworks, and methodologies of the established genomic tests, including the Oncotype Dx Breast Recurrence Score®, MammaPrint, Prosigna®, EndoPredict®, and Breast Cancer Index (BCI). Among these tests, Prosigna and EndoPredict® have at present been validated only on a prognostic level, while Oncotype Dx, MammaPrint, and BCI hold both a prognostic and predictive role. Oncologists and pathologists engaged in the management of EBC will find in this review a thorough comparison of available genomic assays, as well as strategies to optimize the utilization of the information derived from them.

Clinical Biomarkers of Tumour Radiosensitivity and Predicting Benefit from Radiotherapy: A Systematic Review

Modern advanced radiotherapy techniques have improved the precision and accuracy of radiotherapy delivery, with resulting plans being highly personalised based on individual anatomy. Adaptation for individual tumour biology remains elusive. There is an unmet need for biomarkers of intrinsic radiosensitivity that can predict tumour response to radiation to facilitate individualised decision-making, dosing and treatment planning. Over the last few decades, the use of high throughput molecular biology technologies has led to an explosion of newly discovered cancer biomarkers. Gene expression signatures are now used routinely in clinic to aid decision-making regarding adjuvant systemic therapy. They have great potential as radiotherapy biomarkers. A previous systematic review published in 2015 reported only five studies of signatures evaluated for their ability to predict radiotherapy benefits in clinical cohorts. This updated systematic review encompasses the expanded number of studies reported in the last decade. An additional 27 studies were identified. In total, 22 distinct signatures were recognised (5 pre-2015, 17 post-2015). Seventeen signatures were 'radiosensitivity' signatures and five were breast cancer prognostic signatures aiming to identify patients at an increased risk of local recurrence and therefore were more likely to benefit from adjuvant radiation. Most signatures (15/22) had not progressed beyond the discovery phase of development, with no suitable validated clinical-grade assay for application. Very few signatures (4/17 'radiosensitivity' signatures) had undergone any laboratory-based biological validation of their ability to predict tumour radiosensitivity. No signatures have been assessed prospectively in a phase III biomarker-led trial to date and none are recommended for routine use in clinical guidelines. A phase III prospective evaluation is ongoing for two breast cancer prognostic signatures. The most promising radiosensitivity signature remains the radiosensitivity index (RSI), which is used to calculate a genomic adjusted radiation dose (GARD). There is an ongoing phase II prospective biomarker-led study of RSI/GARD in triple negative breast cancer. The results of these trials are eagerly anticipated over the coming years. Future work in this area should focus on (1) robust biological validation; (2) building biobanks alongside large radiotherapy randomised controlled trials with dose variance (to demonstrate an interaction between radiosensitivity signature and dose); (3) a validation of clinical-grade cost-effective assays that are deliverable within current healthcare infrastructure; and (4) an integration with biomarkers of other determinants of radiation response.

Clinical Use of Molecular Biomarkers in Canine and Feline Oncology: Current and Future

Molecular biomarkers are central to personalised medicine for human cancer patients. It is gaining traction as part of standard veterinary clinical practice for dogs and cats with cancer. Molecular biomarkers can be somatic or germline genomic alterations and can be ascertained from tissues or body fluids using various techniques. This review discusses how these genomic alterations can be determined and the findings used in clinical settings as diagnostic, prognostic, predictive, and screening biomarkers. We showcase the somatic and germline genomic alterations currently available to date for testing dogs and cats in a clinical setting, discussing their utility in each biomarker class. We also look at some emerging molecular biomarkers that are promising for clinical use. Finally, we discuss the hurdles that need to be overcome in going 'bench to bedside', i.e., the translation from discovery of genomic alterations to adoption by veterinary clinicians. As we understand more of the genomics underlying canine and feline tumours, molecular biomarkers will undoubtedly become a mainstay in delivering precision veterinary care to dogs and cats with cancer.

BIRC5 expression by race, age and clinical factors in breast cancer patients

PURPOSE

Survivin/BIRC5 is a proliferation marker that is associated with poor prognosis in breast cancer and an attractive therapeutic target. However, BIRC5 has not been well studied among racially diverse populations where aggressive breast cancers are prevalent.

EXPERIMENTAL DESIGN

We studied BIRC5 expression in association with clinical and demographic variables and as a predictor of recurrence in 2174 participants in the Carolina Breast Cancer Study (CBCS), a population-based study that oversampled Black (n = 1113) and younger (< 50 years; n = 1137) participants with breast cancer. For comparison, similar analyses were conducted in The Cancer Genome Atlas [TCGA N = 1094, Black (n = 183), younger (n = 295)]. BIRC5 was evaluated as a continuous and categorical variable (highest quartile vs. lower three quartiles).

RESULTS

Univariate, continuous BIRC5 expression was higher in breast tumors from Black women relative to non-Black women in both estrogen receptor (ER)-positive and ER-negative tumors and in analyses stratified by stage (i.e., within Stage I, Stage II, and Stage III/IV tumors). Within CBCS and TCGA, BIRC5-high was associated with young age (< 50 years) and Black race, as well as hormone receptor-negative tumors, non-Luminal A PAM50 subtypes, advanced stage, and larger tumors (> 2 cm). Relative to BIRC5-low, BIRC5-high tumors were associated with poor 5-year recurrence-free survival (RFS) among ER-positive tumors, both in unadjusted models [HR (95% CI): 2.7 (1.6, 4.6)] and after adjustment for age and stage [Adjusted HR (95% CI): 1.87 (1.07, 3.25)]. However, this relationship was not observed among ER-negative tumors [Crude HR (95% CI): 0.7 (0.39, 1.2); Adjusted HR (95% CI): 0.67 (0.37, 1.2)].

CONCLUSION

Black and younger women with breast cancer have a higher burden of BIRC5-high tumors than older and non-Black women. Emerging anti-survivin treatment strategies may be an important future direction for equitable breast cancer outcomes.

Caveolin-1 gene expression provides additional prognostic information combined with PAM50 risk of recurrence (ROR) score in breast cancer

Combining information from the tumor microenvironment (TME) with PAM50 Risk of Recurrence (ROR) score could improve breast cancer prognostication. Caveolin-1 (CAV1) is a marker of an active TME. CAV1 is a membrane protein involved in cell signaling, extracellular matrix organization, and tumor-stroma interactions. We sought to investigate CAV1 gene expression in relation to PAM50 subtypes, ROR score, and their joint prognostic impact. CAV1 expression was compared between PAM50 subtypes and ROR categories in two cohorts (SCAN-B, n = 5326 and METABRIC, n = 1980). CAV1 expression was assessed in relation to clinical outcomes using Cox regression and adjusted for clinicopathological predictors. Effect modifications between CAV1 expression and ROR categories on clinical outcome were investigated using multiplicative and additive two-way interaction analyses. Differential gene expression and gene set enrichment analyses were applied to compare high and low expressing CAV1 tumors. All samples expressed CAV1 with the highest expression in the Normal-like subtype. Gene modules consistent with epithelial-mesenchymal transition (EMT), hypoxia, and stromal activation were associated with high CAV1 expression. CAV1 expression was inversely associated with ROR category. Interactions between CAV1 expression and ROR categories were observed in both cohorts. High expressing CAV1 tumors conferred worse prognosis only within the group classified as ROR high. ROR gave markedly different prognostic information depending on the underlying CAV1 expression. CAV1, a potential mediator between the malignant cells and TME, could be a useful biomarker that enhances and further refines PAM50 ROR risk stratification in patients with ROR high tumors and a potential therapeutic target.

Plasma Circulating mRNA Profile for the Non-Invasive Diagnosis of Colorectal Cancer Using NanoString Technologies

Colorectal cancer (CRC) is one of the most prevalent cancers and the second leading cause of cancer deaths in developed countries. Early CRC may have no symptoms and symptoms usually appear with more advanced diseases. Regular screening can identify people who are at increased risk of CRC in order to offer earlier treatment. A cost-effective non-invasive platform for the screening and monitoring of CRC patients allows early detection and appropriate treatment of the disease, and the timely application of adjuvant therapy after surgical operation is needed. In this study, a cohort of 71 plasma samples that include 48 colonoscopy- and histopathology-confirmed CRC patients with TNM stages I to IV were recruited between 2017 and 2019. Plasma mRNA profiling was performed in CRC patients using NanoString nCounter. Normalized data were analyzed using a Mann-Whitney U test to determine statistically significant differences between samples from CRC patients and healthy subjects. A multiple-group comparison of clinical phenotypes was performed using the Kruskal-Wallis H test for statistically significant differences between multiple groups. Among the 27 selected circulating mRNA markers, all of them were found to be overexpressed (gene expression fold change > 2) in the plasma of patients from two or more CRC stages. In conclusion, NanoString-based targeted plasma CRC-associated mRNAs circulating the marker panel that can significantly distinguish CRC patients from a healthy population were developed for the non-invasive diagnosis of CRC using peripheral blood samples.

Few-shot genes selection: subset of PAM50 genes for breast cancer subtypes classification

BACKGROUND

In recent years, researchers have made significant strides in understanding the heterogeneity of breast cancer and its various subtypes. However, the wealth of genomic and proteomic data available today necessitates efficient frameworks, instruments, and computational tools for meaningful analysis. Despite its success as a prognostic tool, the PAM50 gene signature's reliance on many genes presents challenges in terms of cost and complexity. Consequently, there is a need for more efficient methods to classify breast cancer subtypes using a reduced gene set accurately.

RESULTS

This study explores the potential of achieving precise breast cancer subtype categorization using a reduced gene set derived from the PAM50 gene signature. By employing a "Few-Shot Genes Selection" method, we randomly select smaller subsets from PAM50 and evaluate their performance using metrics and a linear model, specifically the Support Vector Machine (SVM) classifier. In addition, we aim to assess whether a more compact gene set can maintain performance while simplifying the classification process. Our findings demonstrate that certain reduced gene subsets can perform comparable or superior to the full PAM50 gene signature.

CONCLUSIONS

The identified gene subsets, with 36 genes, have the potential to contribute to the development of more cost-effective and streamlined diagnostic tools in breast cancer research and clinical settings.

B-Cell-Mediated Immunity Predicts Survival of Patients With Estrogen Receptor-Positive Breast Cancer

PURPOSE

The estrogen receptor-positive (ER+) breast cancer (BC), which constitutes the majority of BC cases, exhibits highly heterogeneous clinical behavior. To aid precision treatments, we aimed to find molecular subtypes of ER+ BC representing the tumor microenvironment and prognosis.

METHODS

We analyzed RNA-seq data of 113 patients with BC and classified them according to the PAM50 intrinsic subtypes using gene expression profiles. Among them, we further focused on 44 patients with luminal-type (ER+) BC for subclassification. The Cancer Genome Atlas (TCGA) data of patients with BC were used as a validation data set to verify the new classification. We estimated the immune cell composition using CIBERSORT and further analyzed its association with clinical or molecular parameters.

RESULTS

Principal component analysis clearly divided the patients into two subgroups separately from the luminal A and B classification. The top differentially expressed genes between the subgroups were distinctly characterized by immunoglobulin and B-cell-related genes. We could also cluster a separate cohort of patients with luminal-type BC from TCGA into two subgroups on the basis of the expression of a B-cell-specific gene set, and patients who were predicted to have high B-cell immune activity had better prognoses than other patients.

CONCLUSION

Our transcriptomic approach emphasize a molecular phenotype of B-cell immunity in ER+ BC that may help to predict disease prognosis. Although further researches are required, B-cell immunity for patients with ER+ BC may be helpful for identifying patients who are good responders to chemotherapy or immunotherapy.

A Dysfunctional T-cell Gene Signature for Predicting Nonresponse to PD-1 Blockade in Non-small Cell Lung Cancer That Is Suitable for Routine Clinical Diagnostics

PURPOSE

Because PD-1 blockade is only effective in a minority of patients with advanced-stage non-small cell lung cancer (NSCLC), biomarkers are needed to guide treatment decisions. Tumor infiltration by PD-1T tumor-infiltrating lymphocytes (TIL), a dysfunctional TIL pool with tumor-reactive capacity, can be detected by digital quantitative IHC and has been established as a novel predictive biomarker in NSCLC. To facilitate translation of this biomarker to the clinic, we aimed to develop a robust RNA signature reflecting a tumor's PD-1T TIL status.

EXPERIMENTAL DESIGN

mRNA expression analysis using the NanoString nCounter platform was performed in baseline tumor samples from 41 patients with advanced-stage NSCLC treated with nivolumab that were selected on the basis of PD-1T TIL infiltration by IHC. Samples were included as a training cohort (n = 41) to develop a predictive gene signature. This signature was independently validated in a second cohort (n = 42). Primary outcome was disease control at 12 months (DC 12 m), and secondary outcome was progression-free and overall survival.

RESULTS

Regularized regression analysis yielded a signature using 12 out of 56 differentially expressed genes between PD-1T IHC-high tumors from patients with DC 12 m and PD-1T IHC-low tumors from patients with progressive disease (PD). In the validation cohort, 6/6 (100%) patients with DC 12 m and 23/36 (64%) with PD were correctly classified with a negative predictive value (NPV) of 100% and a positive predictive value of 32%.

CONCLUSIONS

The PD-1T mRNA signature showed a similar high sensitivity and high NPV as the digital IHC quantification of PD-1T TIL. This finding provides a straightforward approach allowing for easy implementation in a routine diagnostic clinical setting.

Targeting the Oxytocin Receptor for Breast Cancer Management: A Niche for Peptide Tracers

Breast cancer is a leading cause of death in women, and its management highly depends on early disease diagnosis and monitoring. This remains challenging due to breast cancer's heterogeneity and a scarcity of specific biomarkers that could predict responses to therapy and enable personalized treatment. This Perspective describes the diagnostic landscape for breast cancer management, molecular strategies targeting receptors overexpressed in tumors, the theranostic potential of the oxytocin receptor (OTR) as an emerging breast cancer target, and the development of OTR-specific optical and nuclear tracers to study, visualize, and treat tumors. A special focus is on the chemistry and pharmacology underpinning OTR tracer development, preclinical in vitro and in vivo studies, challenges, and future directions. The use of peptide-based tracers targeting upregulated receptors in cancer is a highly promising strategy complementing current diagnostics and therapies and providing new opportunities to improve cancer management and patient survival.

Patient-specific analysis of co-expression to measure biological network rewiring in individuals

To effectively understand the underlying mechanisms of disease and inform the development of personalized therapies, it is critical to harness the power of differential co-expression (DCE) network analysis. Despite the promise of DCE network analysis in precision medicine, current approaches have a major limitation: they measure an average differential network across multiple samples, which means the specific etiology of individual patients is often overlooked. To address this, we present Cosinet, a DCE-based single-sample network rewiring degree quantification tool. By analyzing two breast cancer datasets, we demonstrate that Cosinet can identify important differences in gene co-expression patterns between individual patients and generate scores for each individual that are significantly associated with overall survival, recurrence-free interval, and other clinical outcomes, even after adjusting for risk factors such as age, tumor size, HER2 status, and PAM50 subtypes. Cosinet represents a remarkable development toward unlocking the potential of DCE analysis in the context of precision medicine.

Profiling canine mammary tumors: A potential model for studying human breast cancer

Despite all clinical progress recorded in the last decades, human breast cancer (HBC) remains a major challenge worldwide both in terms of its incidence and its management. Canine mammary tumors (CMTs) share similarities with HBC and represent an alternative model for HBC. The utility of the canine model in studying HBC relies on their common features, include spontaneous development, subtype classification, mutational profile, alterations in gene expression profile, and incidence/prevalence. This review describes the similarities between CMTs and HBC regarding genomic landscape, microRNA expression alteration, methylation, and metabolomic changes occurring during mammary gland carcinogenesis. The primary purpose of this review is to highlight the advantages of using the canine model as a translational animal model for HBC research and to investigate the challenges and limitations of this approach.

A Research Protocol for a Phase II Single-Arm Clinical Trial Assessing the Feasibility and Efficacy of Neoadjuvant Anastrozole in Patients With Luminal Breast Cancer and Low Proliferative Index: The ANNE Trial

INTRODUCTION

Neoadjuvant endocrine therapy (NET) is recommended for the treatment of invasive breast cancer (BC), particularly luminal subtypes, in locally advanced stages. Previous randomized studies have demonstrated the benefits of aromatase inhibitors in this context. However, NET is typically reserved for elderly or frail patients who may not tolerate neoadjuvant chemotherapy. Identifying non-responsive patients early and extending treatment for responsive ones would be ideal, yet optimal strategies are awaited.

AIMS

This non-randomized phase 2 clinical trial aims to assess NET feasibility and efficacy in postmenopausal stage II and III luminal BC patients, identifying predictive therapeutic response biomarkers. Efficacy will be gauged by patients with Ki67 ≤ 10% after 4 weeks and Preoperative Endocrine Prognostic Index (PEPI) scores 0 post-surgery. Study feasibility will be determined by participation acceptance rate (recruitment rate ≥50%) and inclusion rate (>2 patients/month).

METHODS

Postmenopausal women with luminal, HER2-tumors in stages II and III undergo neoadjuvant anastrozole treatment, evaluating continuing NET or receiving chemotherapy through early Ki67 analysis after 2 to 4 weeks. The study assesses NET extension for up to 10 months, using serial follow-ups with standardized breast ultrasound and clinical criteria-based NET suspension. Clinical and pathological responses will be measured overall and in the luminal tumor A subgroup. Toxicity, health-related quality of life, and circulating biomarkers predicting early NET response will also be evaluated.

Correlation between breast cancer subtypes determined by immunohistochemistry and n-COUNTER PAM50 assay: a real-world study

PURPOSE

Molecular subtyping based on gene expression profiling (i.e., PAM50 assay) aids in determining the prognosis and treatment of breast cancer (BC), particularly in hormone receptor (HR)-positive/human epidermal growth factor receptor 2 (HER2)-negative tumors, where luminal A and B subtypes have different prognoses and treatments. Several surrogate classifications have been proposed for distinguishing between the luminal A and B subtypes. This study determines the accuracy of local immunohistochemistry (IHC) techniques for classifying HR-positive/HER2-negative (HR+/HER2-) tumors according to intrinsic subtypes using the nCOUNTER PAM50 assay as reference and the HR status definition according the ASCO/CAP recommendations.

METHODS

Molecular subtypes resulting from nCOUNTER PAM50 performed in our laboratory between 2014 and 2020 were correlated with three different proxy surrogates proposed in the literature based on ER, PR, HER2, and Ki67 expression with different cut-off values. Concordance was measured using the level of agreement and kappa statistics.

RESULTS

From 1049 samples with the nCOUNTER test, 679 and 350 were luminal A and B subtypes, respectively. Only a poor-to-fair correlation was observed between the three proxy surrogates and real genomic subtypes as determined by nCOUNTER PAM50. Moreover, 5-11% and 18-36% of the nCOUNTER PAM50 luminal B and A tumors were classified as luminal A and B, respectively, by these surrogates.

CONCLUSION

The concordance between luminal subtypes determined by three different IHC-based classifiers and the nCOUNTER PAM50 assay was suboptimal. Thus, a significant proportion of luminal A and B tumors as determined by the surrogate classifiers could be undertreated or over-treated.

Biomarkers in Cancer Detection, Diagnosis, and Prognosis

Biomarkers are vital in healthcare as they provide valuable insights into disease diagnosis, prognosis, treatment response, and personalized medicine. They serve as objective indicators, enabling early detection and intervention, leading to improved patient outcomes and reduced costs. Biomarkers also guide treatment decisions by predicting disease outcomes and facilitating individualized treatment plans. They play a role in monitoring disease progression, adjusting treatments, and detecting early signs of recurrence. Furthermore, biomarkers enhance drug development and clinical trials by identifying suitable patients and accelerating the approval process. In this review paper, we described a variety of biomarkers applicable for cancer detection and diagnosis, such as imaging-based diagnosis (CT, SPECT, MRI, and PET), blood-based biomarkers (proteins, genes, mRNA, and peptides), cell imaging-based diagnosis (needle biopsy and CTC), tissue imaging-based diagnosis (IHC), and genetic-based biomarkers (RNAseq, scRNAseq, and spatial transcriptomics).

Predicting early breast cancer recurrence from histopathological images in the Carolina Breast Cancer Study

Approaches for rapidly identifying patients at high risk of early breast cancer recurrence are needed. Image-based methods for prescreening hematoxylin and eosin (H&E) stained tumor slides could offer temporal and financial efficiency. We evaluated a data set of 704 1-mm tumor core H&E images (2-4 cores per case), corresponding to 202 participants (101 who recurred; 101 non-recurrent matched on age and follow-up time) from breast cancers diagnosed between 2008-2012 in the Carolina Breast Cancer Study. We leveraged deep learning to extract image information and trained a model to identify recurrence. Cross-validation accuracy for predicting recurrence was 62.4% [95% CI: 55.7, 69.1], similar to grade (65.8% [95% CI: 59.3, 72.3]) and ER status (66.3% [95% CI: 59.8, 72.8]). Interestingly, 70% (19/27) of early-recurrent low-intermediate grade tumors were identified by our image model. Relative to existing markers, image-based analyses provide complementary information for predicting early recurrence.

Detection of high-risk patients resistant to CDK4/6 inhibitors with hormone receptor-positive HER2-negative advanced and metastatic breast cancer in Japan (KBCSG-TR-1316)

BACKGROUND

Cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) improve the prognosis of hormone receptor-positive HER2-negative advanced/metastatic breast cancer (HR+/HER2- mBC). However, some cancers show resistance to CDK4/6i and have a poor prognosis. The non-luminal disease score (NOLUS) was developed to predict non-luminal disease using immunohistochemical analysis.

METHODS

The association between the efficacy of CDK4/6i and NOLUS was investigated by evaluating pathological and clinical data, including real-world progression-free survival (rw-PFS) and overall survival (OS). Real-world data of patients with HR+/HER2- mBC who received CDK4/6i therapy [palbociclib or abemaciclib] as first- or second-line endocrine treatments was obtained. NOLUS was calculated using the formula: NOLUS (0-100) = - 0.45 × estrogen receptor (ER) (%) - 0.28 × progesterone receptor (PR) (%) + 0.27 × Ki67(%) + 73, and the patients were divided into two groups: NOLUS-positive (≥ 51.38) and NOLUS-negative (< 51.38).

RESULTS

Of the 300 patients, 28 (9.3%) were NOLUS-positive, and 272 (90.7%) were NOLUS-negative. The expression rates (%) of ER and PgR in NOLUS-positive patients were lower than those in NOLUS-negative patients (p < 0.001). Ki67 expression was higher in NOLUS-positive patients. There were statistically significant differences in prognosis (rw-PFS and OS) between the two groups. Moreover, NOLUS-negative patients showed statistically better rw-PFS with first-line therapy than second-line therapy. However, NOLUS-positive patients showed poor prognoses with both the first and second therapeutic lines, suggesting CDK4/6i inefficacy for NOLUS-positive patients.

CONCLUSIONS

The efficacy and prognosis of CDK4/6i significantly differed between the NOLUS-positive and NOLUS-negative patients. This feasible method can predict patients with HR+/HER2- mBC resistant to CDK4/6i and help select a better therapeutic approach to overcome resistance.

Precision medicine in extended adjuvant endocrine therapy for breast cancer

PURPOSE OF REVIEW

In this review, the evolving role of currently available genomic assays for hormone receptor-positive, early-stage breast cancer in the selection of patients for extended adjuvant endocrine therapy will be discussed.

RECENT FINDINGS

Several studies have investigated the prognostic performance of the Oncotype DX, Breast Cancer Index (BCI), Prosigna, and EndoPredict genomic assays in the late recurrence setting (>5 years after diagnosis), beyond standardly used clinicopathologic parameters, with mixed results. Recently, BCI has also been validated to predict the likelihood of benefit from extended endocrine therapy, though certain data limitations may need to be addressed to justify routine use in clinical practice.

SUMMARY

Even after 5 years of adjuvant endocrine therapy, patients with hormone receptor-positive breast cancer have a significant risk for late recurrence, including distant metastases, that might be prevented with longer durations of endocrine therapy. However, the added toxicity and variable benefit derived from extended endocrine therapy make optimal patient selection crucial. Genomic assays are in development to risk-stratify patients for late recurrence and determine efficacy of extended endocrine therapy, with the aim to help guide extended endocrine therapy decisions for clinicians and individualize treatment strategies for patients.

Identification of tumor-agnostic biomarkers for predicting prostate cancer progression and biochemical recurrence

The diverse clinical outcomes of prostate cancer have led to the development of gene signature assays predicting disease progression. Improved prostate cancer progression biomarkers are needed as current RNA biomarker tests have varying success for intermediate prostate cancer. Interest grows in universal gene signatures for invasive carcinoma progression. Early breast and prostate cancers share characteristics, including hormone dependence and BRCA1/2 mutations. Given the similarities in the pathobiology of breast and prostate cancer, we utilized the NanoString BC360 panel, comprising the validated PAM50 classifier and pathway-specific signatures associated with general tumor progression as well as breast cancer-specific classifiers. This retrospective cohort of primary prostate cancers (n=53) was stratified according to biochemical recurrence (BCR) status and the CAPRA-S to identify genes related to high-risk disease. Two public cohort (TCGA-PRAD and GSE54460) were used to validate the results. Expression profiling of our cohort uncovered associations between PIP and INHBA with BCR and high CAPRA-S score, as well as associations between VCAN, SFRP2, and THBS4 and BCR. Despite low levels of the ESR1 gene compared to AR, we found strong expression of the ER signaling signature, suggesting that BCR may be driven by ER-mediated pathways. Kaplan-Meier and univariate Cox proportional hazards regression analysis indicated the expression of ESR1, PGR, VCAN, and SFRP2 could predict the occurrence of relapse events. This is in keeping with the pathways represented by these genes which contribute to angiogenesis and the epithelial-mesenchymal transition. It is likely that VCAN works by activating the stroma and remodeling the tumor microenvironment. Additionally, SFRP2 overexpression has been associated with increased tumor size and reduced survival rates in breast cancer and among prostate cancer patients who experienced BCR. ESR1 influences disease progression by activating stroma, stimulating stem/progenitor prostate cancer, and inducing TGF-β. Estrogen signaling may therefore serve as a surrogate to AR signaling during progression and in hormone-refractory disease, particularly in prostate cancer patients with stromal-rich tumors. Collectively, the use of agnostic biomarkers developed for breast cancer stratification has facilitated a precise clinical classification of patients undergoing radical prostatectomy and highlighted the therapeutic potential of targeting estrogen signaling in prostate cancer.

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 Role of Nodes and Nodal Assessment in Diagnosis, Treatment and Prediction in ER+, Node-Positive Breast Cancer

The majority of breast cancers are oestrogen receptor-positive (ER+). In ER+ cancers, oestrogen acts as a disease driver, so these tumours are likely to be susceptible to endocrine therapy (ET). ET works by blocking the hormone's synthesis or effect. A significant number of patients diagnosed with breast cancer will have the spread of tumour cells into regional lymph nodes either at the time of diagnosis, or as a recurrence some years later. Patients with node-positive disease have a poorer prognosis and can respond less well to ET. The nodal metastases may be genomically similar or, as is becoming more evident, may differ from the primary tumour. However, nodal metastatic disease is often not assessed, and treatment decisions are almost always based on biomarkers evaluated in the primary tumour. This review will summarise the evidence in the field on ER+, node-positive breast cancer, including diagnosis, treatment, prognosis and predictive tools.

Molecular Classification of Breast Cancer

Breast carcinomas classified based on traditional morphologic assessment provide useful prognostic information. Although morphology is still the gold standard of classification, recent advances in molecular technologies have enabled the classification of these tumors into four distinct subtypes based on its intrinsic molecular profile that provide both predictive and prognostic information. This article describes the association between the different molecular subtypes with the histologic subtypes of breast cancer and illustrates how these subtypes may affect the appearance of tumors on imaging studies.

Vis-SPLIT: Interactive Hierarchical Modeling for mRNA Expression Classification

We propose an interactive visual analytics tool, Vis-SPLIT, for partitioning a population of individuals into groups with similar gene signatures. Vis-SPLIT allows users to interactively explore a dataset and exploit visual separations to build a classification model for specific cancers. The visualization components reveal gene expression and correlation to assist specific partitioning decisions, while also providing overviews for the decision model and clustered genetic signatures. We demonstrate the effectiveness of our framework through a case study and evaluate its usability with domain experts. Our results show that Vis-SPLIT can classify patients based on their genetic signatures to effectively gain insights into RNA sequencing data, as compared to an existing classification system.

Neoadjuvant endocrine therapy in ER-positive breast cancer: evolution, indication, and tailored treatment strategy

In recent years, endocrine therapy (ET), an effective systemic treatment for the management of estrogen receptor (ER)-positive breast cancers, has regained interest as a neoadjuvant therapy based on evidence that ET can fulfill the aim of neoadjuvant systemic treatment for tumor shrinkage as well as elucidate important clinical information on endocrine sensitivity that enables the prognostication of patients. Moreover, neoadjuvant endocrine therapy (NET) potentially provides an opportunity for early assessment of the clinical efficacy of novel agents. Furthermore, recently reported trials have generated evidence for a more tailored approach for perioperative management of ER-positive breast cancer using clinical and molecular biomarkers, and this has provided a rationale that enables the broadening of clinical indications for NET. This review discusses the current evidence for NET, the evolution of NET trials, clinical indications, and NET-based treatment strategies.

Tamoxifen-predictive value of gene expression signatures in premenopausal breast cancer: data from the randomized SBII:2 trial

BACKGROUND

Gene expression (GEX) signatures in breast cancer provide prognostic information, but little is known about their predictive value for tamoxifen treatment. We examined the tamoxifen-predictive value and prognostic effects of different GEX signatures in premenopausal women with early breast cancer.

METHODS

RNA from formalin-fixed paraffin-embedded tumor tissue from premenopausal women randomized between two years of tamoxifen treatment and no systemic treatment was extracted and successfully subjected to GEX profiling (n = 437, NanoString Breast Cancer 360™ panel). The median follow-up periods for a recurrence-free interval (RFi) and overall survival (OS) were 28 and 33 years, respectively. Associations between GEX signatures and tamoxifen effect were assessed in patients with estrogen receptor-positive/human epidermal growth factor receptor 2-negative (ER+ /HER2-) tumors using Kaplan-Meier estimates and Cox regression. The prognostic effects of GEX signatures were studied in the entire cohort. False discovery rate adjustments (q-values) were applied to account for multiple hypothesis testing.

RESULTS

In patients with ER+/HER2- tumors, FOXA1 expression below the median was associated with an improved effect of tamoxifen after 10 years with regard to RFi (hazard ratio [HR]FOXA1(high) = 1.04, 95% CI = 0.61-1.76, HRFOXA1(low) = 0.30, 95% CI = 0.14-0.67, qinteraction = 0.0013), and a resembling trend was observed for AR (HRAR(high) = 1.15, 95% CI = 0.60-2.20, HRAR(low) = 0.42, 95% CI = 0.24-0.75, qinteraction = 0.87). Similar patterns were observed for OS. Tamoxifen was in the same subgroup most beneficial for RFi in patients with low ESR1 expression (HRRFi ESR1(high) = 0.76, 95% CI = 0.43-1.35, HRRFi, ESR1(low) = 0.56, 95% CI = 0.29-1.06, qinteraction = 0.37). Irrespective of molecular subtype, higher levels of ESR1, Mast cells, and PGR on a continuous scale were correlated with improved 10 years RFi (HRESR1 = 0.80, 95% CI = 0.69-0.92, q = 0.005; HRMast cells = 0.74, 95% CI = 0.65-0.85, q < 0.0001; and HRPGR = 0.78, 95% CI = 0.68-0.89, q = 0.002). For BC proliferation and Hypoxia, higher scores associated with worse outcomes (HRBCproliferation = 1.54, 95% CI = 1.33-1.79, q < 0.0001; HRHypoxia = 1.38, 95% CI = 1.20-1.58, q < 0.0001). The results were similar for OS.

CONCLUSIONS

Expression of FOXA1 is a promising predictive biomarker for tamoxifen effect in ER+/HER2- premenopausal breast cancer. In addition, each of the signatures BC proliferation, Hypoxia, Mast cells, and the GEX of AR, ESR1, and PGR had prognostic value, also after adjusting for established prognostic factors. Trial registration This trial was retrospectively registered in the ISRCTN database the 6th of December 2019, trial ID: https://clinicaltrials.gov/ct2/show/ISRCTN12474687 .

A supervised Bayesian factor model for the identification of multi-omics signatures

MOTIVATION

Predictive biological signatures provide utility as biomarkers for disease diagnosis and prognosis, as well as prediction of responses to vaccination or therapy. These signatures are iden-tified from high-throughput profiling assays through a combination of dimensionality reduction and machine learning techniques. The genes, proteins, metabolites, and other biological analytes that compose signatures also generate hypotheses on the underlying mechanisms driving biological responses, thus improving biological understanding. Dimensionality reduction is a critical step in signature discovery to address the large number of analytes in omics datasets, especially for multi-omics profiling studies with tens of thousands of measurements. Latent factor models, which can account for the structural heterogeneity across diverse assays, effectively integrate multi-omics data and reduce dimensionality to a small number of factors that capture correlations and associations among measurements. These factors provide biologically interpretable features for predictive model-ing. However, multi-omics integration and predictive modeling are generally performed independent-ly in sequential steps, leading to suboptimal factor construction. Combining these steps can yield better multi-omics signatures that are more predictive while still being biologically meaningful.

RESULTS

We developed a supervised variational Bayesian factor model that extracts multi-omics signatures from high-throughput profiling datasets that can span multiple data types. Signature-based multiPle-omics intEgration via lAtent factoRs (SPEAR) adaptively determines factor rank, emphasis on factor structure, data relevance and feature sparsity. The method improves the recon-struction of underlying factors in synthetic examples and prediction accuracy of COVID-19 severity and breast cancer tumor subtypes.

AVAILABILITY

SPEAR is a publicly available R-package hosted at https://bitbucket.org/kleinstein/SPEAR.

The Role and Therapeutic Targeting of CCR5 in Breast Cancer

The G-protein-coupled receptor C-C chemokine receptor 5 (CCR5) functions as a co-receptor for the entry of HIV into immune cells. CCR5 binds promiscuously to a diverse array of ligands initiating cell signaling that includes guided migration. Although well known to be expressed on immune cells, recent studies have shown the induction of CCR5 on the surface of breast cancer epithelial cells. The function of CCR5 on breast cancer epithelial cells includes the induction of aberrant cell survival signaling and tropism towards chemo attractants. As CCR5 is not expressed on normal epithelium, the receptor provides a potential useful target for therapy. Inhibitors of CCR5 (CCR5i), either small molecules (maraviroc, vicriviroc) or humanized monoclonal antibodies (leronlimab) have shown anti-tumor and anti-metastatic properties in preclinical studies. In early clinical studies, reviewed herein, CCR5i have shown promising results and evidence for effects on both the tumor and the anti-tumor immune response. Current clinical studies have therefore included combination therapy approaches with checkpoint inhibitors.

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.

HER2-Low Breast Cancer: Current Landscape and Future Prospects

More than 50% of breast cancers are currently defined as "Human epidermal growth factor receptor 2 (HER2) low breast cancer (BC)", with HER2 immunohistochemistry (IHC) scores of +1 or +2 with a negative fluorescence in situ hybridization (FISH) test. In most studies that compared the clinical and biological characteristics of HER2-low BC with HER2-negative BC, HER2-low was not associated with unique clinical and molecular characteristics, and it seems that the importance of HER2 in these tumors is being a docking site for the antibody portion of antibody drug conjugates (ADCs). Current pathological methods may underestimate the proportion of BCs that express low levels of HER2 due to analytical limitations and tumor heterogeneity. In this review we summarize and contextualize the most recent literature on HER2-low breast cancers, including clinical and translational studies We also review the challenges of assessing low HER2 expression in BC and discuss the current and future therapeutic landscape for these tumors.