Genomic features of rapid versus late relapse in triple negative breast cancer

Identifying and Treating Those at Risk: Disparities in Rapid Relapse Among TNBC Patients in the National Cancer Database

PURPOSE

This study was designed to characterize features of rapid relapse TNBC (rrTNBC), an aggressive, poor prognosis breast cancer subset using the National Cancer Database (NCDB).

METHODS

Patients diagnosed with TNBC between 2010 and 2019 within NCDB were included in analyses. rrTNBC was defined as all-cause mortality ≤24 months from diagnosis. Patient demographic, tumor, and treatment association with rrTNBC were evaluated in univariate, bivariate analyses, and multiple logistic regression models. Two-part models are used to compare receipt of treatment (i.e., receipt of both chemotherapy and breast surgery) versus not in its relationship with rrTNBC.

RESULTS

Overall, 14.5% of patients were categorized as rrTNBC. Age older than 75 years (-41.3%), Black race (-1.4%), Medicare (-2.6%), and Charlson-Deyo score ≥2 (-4.9%) were associated with a lower probability of receiving both chemotherapy and breast surgery. Not receiving both treatments (vs. receiving both chemotherapy and breast surgery) was associated with a two-to-three-fold higher probability of rrTNBC among patients aged older than 75 years (16.6% vs. 6%), having Medicare (3.6% vs. 1.6%), and Charlson-Deyo score ≥2 (16.6% vs. 5.9%).

CONCLUSIONS

Age, insurance, and comorbidity were related to a lower likelihood of treatment; yet receiving treatment reduced the risk of rrTNBC threefold for each. These findings might be valuable to inform clinical care delivery, as well as future research that examines treatment protocols among diverse patients.

IMpassion132 double-blind randomised phase III trial of chemotherapy with or without atezolizumab for early relapsing unresectable locally advanced or metastatic triple-negative breast cancer

BACKGROUND

Immune checkpoint inhibitors improve the efficacy of first-line chemotherapy for patients with programmed death-ligand 1 (PD-L1)-positive unresectable locally advanced/metastatic triple-negative breast cancer (aTNBC), but randomised data in rapidly relapsing aTNBC are scarce.

PATIENTS AND METHODS

IMpassion132 (NCT03371017) enrolled patients with aTNBC relapsing <12 months after last chemotherapy dose (anthracycline and taxane required) or surgery for early TNBC. PD-L1 status was centrally assessed using SP142 before randomisation. Initially patients were enrolled irrespective of PD-L1 status. From August 2019, enrolment was restricted to PD-L1-positive (tumour immune cell ≥1%) aTNBC. Patients were randomised 1:1 to placebo or atezolizumab 1200 mg every 21 days with investigator-selected chemotherapy until disease progression or unacceptable toxicity. Stratification factors were chemotherapy regimen (carboplatin plus gemcitabine or capecitabine monotherapy), visceral (lung and/or liver) metastases and (initially) PD-L1 status. The primary endpoint was overall survival (OS), tested hierarchically in patients with PD-L1-positive tumours and then, if positive, in the modified intent-to-treat (mITT) population (all-comer patients randomised pre-August 2019). Secondary endpoints included progression-free survival (PFS), objective response rate (ORR) and safety.

RESULTS

Among 354 patients with rapidly relapsing PD-L1-positive aTNBC, 68% had a disease-free interval of <6 months and 73% received carboplatin/gemcitabine. The OS hazard ratio was 0.93 (95% confidence interval 0.73-1.20, P = 0.59; median 11.2 months with placebo versus 12.1 months with atezolizumab). mITT and subgroup results were consistent. Median PFS was 4 months across treatment arms and populations. ORRs were 28% with placebo versus 40% with atezolizumab. Adverse events (predominantly haematological) were similar between arms and as expected with atezolizumab plus carboplatin/gemcitabine or capecitabine following recent chemotherapy exposure.

CONCLUSIONS

OS, which is dismal in patients with TNBC relapsing within <12 months, was not improved by adding atezolizumab to chemotherapy. A biology-based definition of intrinsic resistance to immunotherapy in aTNBC is urgently needed to develop novel therapies for these patients in next-generation clinical trials.

TROPION-Breast03: a randomized phase III global trial of datopotamab deruxtecan ± durvalumab in patients with triple-negative breast cancer and residual invasive disease at surgical resection after neoadjuvant therapy

Background

Despite advances in the treatment of early triple-negative breast cancer (TNBC), patients with residual invasive disease after neoadjuvant therapy have a high risk of disease recurrence and worse survival outcomes than those who have pathological complete response (pCR). Improving outcomes in early TNBC remains an unmet need requiring new adjuvant treatment approaches. Datopotamab deruxtecan (Dato-DXd) is an antibody-drug conjugate comprising a humanized anti-trophoblast cell-surface antigen 2 immunoglobulin G1 (IgG1) monoclonal antibody attached via a plasma-stable, cleavable linker to a potent topoisomerase I inhibitor payload, with activity observed in advanced TNBC.

Objectives

TROPION-Breast03 is an ongoing phase III study evaluating the efficacy and safety of Dato-DXd alone or combined with durvalumab versus standard-of-care therapy as adjuvant treatment in patients with stage I-III TNBC with residual invasive disease at surgical resection following neoadjuvant treatment.

Methods and design

Eligible patients, aged ⩾18 years, will be randomized in a 2:1:2 ratio to receive Dato-DXd [6 mg/kg intravenously (IV) every 3 weeks (Q3W); eight cycles] and durvalumab (1120 mg IV Q3W; nine cycles), Dato-DXd monotherapy (6 mg/kg IV Q3W), or investigator's choice of therapy (ICT; capecitabine, pembrolizumab, or capecitabine and pembrolizumab). The primary endpoint is invasive disease-free survival (iDFS) for Dato-DXd and durvalumab versus ICT. Key secondary endpoints include safety, distant disease-free survival, and overall survival for Dato-DXd and durvalumab versus ICT and iDFS for Dato-DXd monotherapy versus ICT.

Ethics

TROPION-Breast03 will be approved by the independent ethics committees or institutional review boards at each study site. All study participants will provide written informed consent.

Discussion

TROPION-Breast03 will help define the potential role of Dato-DXd in the treatment of patients with early-stage TNBC who do not have pCR after neoadjuvant therapy.

Trial registration

ClinicalTrials.gov identifier: NCT05629585 (registration date: 29 November 2022).

Low Hospital Volume Is Associated with Higher All-Cause Mortality in Black Women with Triple Negative Breast Cancer

INTRODUCTION

This study examines the association between hospital volume and all-cause mortality in Black women with triple negative breast cancer (TNBC) who received surgery and chemotherapy.

METHODS

Black women ages 18+ with stage I-III TNBC who received both surgery and chemotherapy were identified in the National Cancer Database (NCDB). Hospital volume was determined using the number of annual breast cancer cases divided by the number of years the hospital participated in the NCDB. Hospital annual volume quartiles ranged from Q1 (lowest) to Q4 (highest). Univariable analysis and multivariable logistic regression modeling with restricted cubic splines examined the effect of hospital volume on all-cause mortality.

RESULTS

Sixteen thousand five hundred fifty-six patients met the study criteria. All-cause mortality incidence was lower at higher volume compared to lower volume hospitals Q1 24.1% (95% CI: 22.8 to 25.4), Q2 21.8% (95% CI: 20.5 to 23.1), Q3 20.9% (95% CI: 19.6 to 22.1), Q4 19.0% (95% CI: 17.7 to 20.1), p<0.001. On multivariable analysis, treatment at the highest hospital volume quartile was associated with a 21% reduction in the odds of death compared to the lowest quartile [Q4 Vs. Q1, OR=0.79 (95% CI: 0.67 to 0.92)]. For every 100-patient increase in annual volume, all-cause mortality was reduced by 4% [OR=0.96 (95% CI: 0.94 to 0.98)]. There was a significant linear dose-dependent relationship between increasing hospital volume and all-cause mortality.

CONCLUSION

Black women treated at high-volume hospitals have lower all-cause mortality than those at low-volume hospitals. Future studies should examine the characteristics of high-volume hospitals associated with improved outcomes.

Cancer stem cells are prevalent in the basal-like 2 and mesenchymal triple-negative breast cancer subtypes in vitro

Background: Triple-negative breast cancer (TNBC) is an aggressive subtype with the most unfavorable clinical outcomes, in part due to tumor heterogeneity, treatment resistance, and tumor relapse. The TNBC subtypes [basal-like 1 (BL1), basal-like 2 (BL2), mesenchymal (M), and luminal androgen receptor (LAR)] are biologically and clinically distinct entities that respond differently to local and systemic therapies. Therefore, we need to have a better understanding of cancer stemness relating to drug-resistant populations in the TNBC subtypes. Methods: Breast cancer stem cell (BCSC) distribution was investigated using an integrated flow cytometry approach with the ALDEFLUOR™ assay (ALDH) and CD24/CD44 antibodies. In total, 27 commercially available cell lines derived from normal and malignant mammary tissue were characterized into differentiated tumor cells and/or BCSC subpopulations (ALDH-CD44+CD24-/low enriched mesenchymal-like BCSCs, ALDH+non-CD44+CD24-/low enriched epithelial-like BCSCs, and highly purified ALDH+CD44+CD24-/low BCSCs). Results: BCSCs were not only enriched in estrogen receptor (ER) negative (mean, 49.6% versus 6.9% in ER+) and TNBC cell lines (51.3% versus 2.1% in Luminal A), but certain BCSC subpopulations (e.g., enriched mesenchymal-like BCSCs) were also significantly more common in the M (64.0% versus 6.2% in BL1; 64.0% versus 0% in LAR) and BL2 (77.4% versus 6.2% in BL1; 77.4% versus 0% in LAR; 77.4% versus 10.4% in TNBC UNS) TNBC subtypes. In contrast, ALDH status alone was not indicative of ER status or BC subtype. Conclusion: Taken together, these findings demonstrate the enrichment of potentially treatment-resistant BCSC subpopulations in the M and BL2 triple-negative breast cancer subtypes.

A new prognostic model including immune biomarkers, genomic proliferation tumor markers (AURKA and MYBL2) and clinical-pathological features optimizes prognosis in neoadjuvant breast cancer patients

Background

Up to 30% of breast cancer (BC) patients treated with neoadjuvant chemotherapy (NCT) will relapse. Our objective was to analyze the predictive capacity of several markers associated with immune response and cell proliferation combined with clinical parameters.

Methods

This was a single-center, retrospective cohort study of BC patients treated with NCT (2001-2010), in whom pretreatment biomarkers were analyzed: neutrophil-to-lymphocyte ratio (NLR) in peripheral blood, CD3+ tumor-infiltrating lymphocytes (TILs), and gene expression of AURKA, MYBL2 and MKI67 using qRT-PCR.

Results

A total of 121 patients were included. Median followup was 12 years. In a univariate analysis, NLR, TILs, AURKA, and MYBL2 showed prognostic value for overall survival. In multivariate analyses, including hormone receptor, HER2 status, and response to NCT, NLR (HR 1.23, 95% CI 1.01-1.75), TILs (HR 0.84, 95% CI 0.73-0.93), AURKA (HR 1.05, 95% CI 1.00-1.11) and MYBL2 (HR 1.19, 95% CI 1.05-1.35) remained as independent predictor variables.

Conclusion

Consecutive addition of these biomarkers to a regression model progressively increased its discriminatory capacity for survival. Should independent cohort studies validate these findings, management of early BC patients may well be changed.

Transcriptome Meta-Analysis of Triple-Negative Breast Cancer Response to Neoadjuvant Chemotherapy

Triple-negative breast cancer (TNBC) is a heterogeneous disease with varying responses to neoadjuvant chemotherapy (NAC). The identification of biomarkers to predict NAC response and inform personalized treatment strategies is essential. In this study, we conducted large-scale gene expression meta-analyses to identify genes associated with NAC response and survival outcomes. The results showed that immune, cell cycle/mitotic, and RNA splicing-related pathways were significantly associated with favorable clinical outcomes. Furthermore, we integrated and divided the gene association results from NAC response and survival outcomes into four quadrants, which provided more insights into potential NAC response mechanisms and biomarker discovery.

Clinicopathologic and sociodemographic factors associated with late relapse triple negative breast cancer in a multivariable logistic model: A multi-institution cohort study

BACKGROUND

Most metastatic recurrences of triple negative breast cancer (TNBC) occur within five years of diagnosis, yet late relapses of TNBC (lrTNBC) do occur. Our objective was to develop a risk prediction model of lrTNBC using readily available clinicopathologic and sociodemographic features.

METHODS

We included patients diagnosed with stage I-III TNBC between 1998 and 2012 at ten academic cancer centers. lrTNBC was defined as relapse or mortality greater than 5 years from diagnosis. Features associated with lrTNBC were included in a multivariable logistic model using backward elimination with a p < 0.10 criterion, with a final multivariable model applied to training (70%) and independent validation (30%) cohorts.

RESULTS

A total 2210 TNBC patients with at least five years follow-up and no relapse before 5 years were included. In final multivariable model, lrTNBC was significantly associated with higher stage at diagnosis (adjusted Odds Ratio [aOR] for stage III vs I, 10.9; 95% Confidence Interval [CI], 7.5-15.9; p < 0.0001) and BMI (aOR for obese vs normal weight, 1.4; 95% CI, 1.0-1.8; p = 0.03). Final model performance was consistent between training (70%) and validation (30%) cohorts.

CONCLUSIONS

A risk prediction model incorporating stage, BMI, and age at diagnosis offers potential utility for identification of patients at risk of development of lrTNBC and warrants further investigation.

Characteristics of recurrence, predictors for relapse and prognosis of rapid relapse triple-negative breast cancer

Background

Triple-negative breast cancer (TNBC) patients who recur at different times are associated with distinct biological characteristics and prognoses. Research on rapid-relapse TNBC (RR-TNBC) is sparse. In this study, we aimed to describe the characteristics of recurrence, predictors for relapse, and prognosis in rrTNBC patients.

Methods

Clinicopathological data of 1584 TNBC patients from 2014 to 2016 were retrospectively reviewed. The characteristics of recurrence were compared between patients with RR-TNBC and slow relapse TNBC(SR-TNBC). All TNBC patients were randomly divided into a training set and a validation set to find predictors for rapid relapse. The multivariate logistic regression model was used to analyze the data of the training set. C-index and brier score analysis for predicting rapid relapse in the validation set was used to evaluate the discrimination and accuracy of the multivariate logistic model. Prognostic measurements were analyzed in all TNBC patients.

Results

Compared with SR-TNBC patients, RR-TNBC patients tended to have a higher T staging, N staging, TNM staging, and low expression of stromal tumor-infiltrating lymphocytes (sTILs). The recurring characteristics were prone to appear as distant metastasis at the first relapse. The first metastatic site was apt to visceral metastasis and less likely to have chest wall or regional lymph node metastasis. Six predictors (postmenopausal status, metaplastic breast cancer,≥pT3 staging,≥pN1 staging, sTIL intermediate/high expression, and Her2 [1+]) were used to construct the predictive model of rapid relapse in TNBC patients. The C-index and brier score in the validation set was 0.861 and 0.095, respectively. This suggested that the predictive model had high discrimination and accuracy. The prognostic data for all TNBC patients showed that RR-TNBC patients had the worst prognosis, followed by SR-TNBC patients.

Conclusion

RR-TNBC patients were associated with unique biological characteristics and worse outcomes compared to non-RR-TNBC patients.

Radiomics-based signature of breast cancer on preoperative contrast-enhanced MRI to predict axillary metastasis

Aim: This study aimed to predict axillary metastasis using radiology features in dynamic contrast-enhanced MRI. Methods: This study included 243 breast lesions confirmed as malignant based on axillary status. Most outcome-predictive features were selected using four machine-learning algorithms. Receiver operating characteristic analysis was used to reflect diagnostic performance. Results: Least absolute shrinkage and selection operator was used to dimensionally reduce 1137 radiomics features to three features. Three optimal radiomics features were used to model construction. The logistic regression model achieved an accuracy of 97% and 85% in the training and test groups. Clinical utility was evaluated using decision curve analysis. Conclusion: The novel combination of radiomics analysis and machine-learning algorithm could predict axillary metastasis and prevent invasive manipulation.

Immune-Related Gene Signatures to Predict the Effectiveness of Chemoimmunotherapy in Triple-Negative Breast Cancer Using Exploratory Subgroup Discovery

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited therapeutic options. Although immunotherapy has shown potential in TNBC patients, clinical studies have only demonstrated a modest response. Therefore, the exploration of immunotherapy in combination with chemotherapy is warranted. In this project we identified immune-related gene signatures for TNBC patients that may explain differences in patients' outcomes after anti-PD-L1+chemotherapy treatment. First, we ran the exploratory subgroup discovery algorithm on the TNBC dataset comprised of 422 patients across 24 studies. Secondly, we narrowed down the search to twelve homogenous subgroups based on tumor mutational burden (TMB, low or high), relapse status (disease-free or recurred), tumor cellularity (high, low and moderate), menopausal status (pre- or post) and tumor stage (I, II and III). For each subgroup we identified a union of the top 10% of genotypic patterns. Furthermore, we employed a multinomial regression model to predict significant genotypic patterns that would be linked to partial remission after anti-PD-L1+chemotherapy treatment. Finally, we uncovered distinct immune cell populations (T-cells, B-cells, Myeloid, NK-cells) for TNBC patients with various treatment outcomes. CD4-Tn-LEF1 and CD4-CXCL13 T-cells were linked to partial remission on anti-PD-L1+chemotherapy treatment. Our informatics pipeline may help to select better responders to chemoimmunotherapy, as well as pinpoint the underlying mechanisms of drug resistance in TNBC patients at single-cell resolution.

A comprehensive genomic and transcriptomic dataset of triple-negative breast cancers

Molecular subtyping of triple-negative breast cancer (TNBC) is essential for understanding the mechanisms and discovering actionable targets of this highly heterogeneous type of breast cancer. We previously performed a large single-center and multiomics study consisting of genomics, transcriptomics, and clinical information from 465 patients with primary TNBC. To facilitate reusing this unique dataset, we provided a detailed description of the dataset with special attention to data quality in this study. The multiomics data were generally of high quality, but a few sequencing data had quality issues and should be noted in subsequent data reuse. Furthermore, we reconduct data analyses with updated pipelines and the updated version of the human reference genome from hg19 to hg38. The updated profiles were in good concordance with those previously published in terms of gene quantification, variant calling, and copy number alteration. Additionally, we developed a user-friendly web-based database for convenient access and interactive exploration of the dataset. Our work will facilitate reusing the dataset, maximize the values of data and further accelerate cancer research.

Measurement(s)	RNA expression profiling • whole-exome sequencing (WES) • somatic mutations • copy number alterations (CNAs)
Technology Type(s)	RNA sequencing • DNA sequencing • OncoScan CNV assay
Factor Type(s)	Intervention or procedure
Sample Characteristic - Organism	Homo sapiens
Sample Characteristic - Location	China

Identifying common transcriptome signatures of cancer by interpreting deep learning models

Background

Cancer is a set of diseases characterized by unchecked cell proliferation and invasion of surrounding tissues. The many genes that have been genetically associated with cancer or shown to directly contribute to oncogenesis vary widely between tumor types, but common gene signatures that relate to core cancer pathways have also been identified. It is not clear, however, whether there exist additional sets of genes or transcriptomic features that are less well known in cancer biology but that are also commonly deregulated across several cancer types.

Results

Here, we agnostically identify transcriptomic features that are commonly shared between cancer types using 13,461 RNA-seq samples from 19 normal tissue types and 18 solid tumor types to train three feed-forward neural networks, based either on protein-coding gene expression, lncRNA expression, or splice junction use, to distinguish between normal and tumor samples. All three models recognize transcriptome signatures that are consistent across tumors. Analysis of attribution values extracted from our models reveals that genes that are commonly altered in cancer by expression or splicing variations are under strong evolutionary and selective constraints. Importantly, we find that genes composing our cancer transcriptome signatures are not frequently affected by mutations or genomic alterations and that their functions differ widely from the genes genetically associated with cancer.

Conclusions

Our results highlighted that deregulation of RNA-processing genes and aberrant splicing are pervasive features on which core cancer pathways might converge across a large array of solid tumor types.

Supplementary Information

The online version contains supplementary material available at (10.1186/s13059-022-02681-3).