The impact of distinct triple-negative breast cancer subtypes on misdiagnosis and diagnostic delay

Prognostic Value of Immunohistochemistry-based Subtyping Before and After Neoadjuvant Chemotherapy in Patients with Triple-negative Breast Cancer

To assess the predictive and prognostic value of a subtyping method based on immunohistochemistry in patients with triple-negative breast cancer (TNBC) treated with neoadjuvant chemotherapy (NAC). This study included patients with TNBC treated with anthracycline- and taxane-based NAC and curative surgery. Immunohistochemical (IHC) subtyping was performed using core needle biopsy specimens before NAC (pre-NAC) and residual tumors after NAC (post-NAC). Logistic regression was performed to identify predictive biomarkers of pathological complete response (pCR). Invasive disease-free survival (iDFS), distant disease-free survival (DDFS), and overall survival (OS) were assessed using the log-rank test and Cox proportional hazards regression. A total of 230 patients were followed up for a median of 59 months. Clinical lymph node status and the pre-NAC subtype were independent predictors of pCR (P=0.006 and 0.005, respectively). The pre-NAC subtype was an independent prognostic factor for long-term survival (iDFS: P < 0.001, DDFS: P=0.010, and OS: P=0.044). Among patients with residual disease (RD) after NAC, approximately 45% of tumors changed their IHC subtype. Furthermore, the post-NAC subtype, but not the pre-NAC subtype, was strongly associated with the survival of patients with RD (iDFS: P < 0.001, DDFS: P=0.005, and OS: P=0.006). The IHC subtype predicted response to NAC and long-term survival in patients with early TNBC. In patients with RD, almost 45% of the tumors changed subtype after NAC. The IHC subtype should be considered when planning additional therapies pre- and post-NAC.

BI-RADS-NET-V2: A Composite Multi-Task Neural Network for Computer-Aided Diagnosis of Breast Cancer in Ultrasound Images With Semantic and Quantitative Explanations

Computer-aided Diagnosis (CADx) based on explainable artificial intelligence (XAI) can gain the trust of radiologists and effectively improve diagnosis accuracy and consultation efficiency. This paper proposes BI-RADS-Net-V2, a novel machine learning approach for fully automatic breast cancer diagnosis in ultrasound images. The BI-RADS-Net-V2 can accurately distinguish malignant tumors from benign ones and provides both semantic and quantitative explanations. The explanations are provided in terms of clinically proven morphological features used by clinicians for diagnosis and reporting mass findings, i.e., Breast Imaging Reporting and Data System (BI-RADS). The experiments on 1,192 Breast Ultrasound (BUS) images indicate that the proposed method improves the diagnosis accuracy by taking full advantage of the medical knowledge in BI-RADS while providing both semantic and quantitative explanations for the decision.

Luminal androgen receptor (LAR) subtype of triple-negative breast cancer: molecular, morphological, and clinical features

According to the classification presented by Lehmann BD (2016), triple-negative breast cancer (TNBC) is a heterogeneous group of malignant tumors with four specific subtypes: basal-like (subtype 1 and subtype 2), mesenchymal, and luminal androgen receptor (LAR) subtypes. The basal-like subtypes of carcinomas predominate in this group, accounting for up to 80% of all cases. Despite the significantly lower proportions of mesenchymal and LAR variants in the group of breast carcinomas with a TNBC profile, such tumors are characterized by aggressive biological behavior. To this end, the LAR subtype is of particular interest, since the literature on such tumors presents different and even contradictory data concerning the disease course and prognosis. This review is devoted to the analysis of the relevant literature, reflecting the main results of studies on the molecular properties and clinical features of the disease course of LAR-type TNBC carcinomas.

Survival outcome assessment for triple-negative breast cancer: a nomogram analysis based on integrated clinicopathological, sonographic, and mammographic characteristics

Objective

This study aimed to incorporate clinicopathological, sonographic, and mammographic characteristics to construct and validate a nomogram model for predicting disease-free survival (DFS) in patients with triple-negative breast cancer (TNBC).

Methods

Patients diagnosed with TNBC at our institution between 2011 and 2015 were retrospectively evaluated. A nomogram model was generated based on clinicopathological, sonographic, and mammographic variables that were associated with 1-, 3-, and 5-year DFS determined by multivariate logistic regression analysis in the training set. The nomogram model was validated according to the concordance index (C-index) and calibration curves in the validation set.

Results

A total of 636 TNBC patients were enrolled and divided into training cohort (n = 446) and validation cohort (n = 190). Clinical factors including tumor size > 2 cm, axillary dissection, presence of LVI, and sonographic features such as angular/spiculated margins, posterior acoustic shadows, and presence of suspicious lymph nodes on preoperative US showed a tendency towards worse DFS. The multivariate analysis showed that no adjuvant chemotherapy (HR = 6.7, 95% CI: 2.6, 17.5, p < 0.0005), higher axillary tumor burden (HR = 2.7, 95% CI: 1.0, 7.1, p = 0.045), and ≥ 3 malignant features on ultrasound (HR = 2.4, CI: 1.1, 5.0, p = 0.021) were identified as independent prognostic factors associated with poorer DFS outcomes. In the nomogram, the C-index was 0.693 for the training cohort and 0.694 for the validation cohort. The calibration plots also exhibited excellent consistency between the nomogram-predicted and actual survival probabilities in both the training and validation cohorts.

Conclusions

Clinical variables and sonographic features were correlated with the prognosis of TNBCs. The nomogram model based on three variables including no adjuvant chemotherapy, higher axillary tumor load, and more malignant sonographic features showed good predictive performance for poor survival outcomes of TNBC.

Key Points

• The absence of adjuvant chemotherapy, heavy axillary tumor load, and malignant-like sonographic features can predict DFS in patients with TNBC.

• Mammographic features of TNBC could not predict the survival outcomes of patients with TNBC.

• The nomogram integrating clinicopathological and sonographic characteristics is a reliable predictive model for the prognostic outcome of TNBC.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-022-08910-4.

Mammographic density to predict response to neoadjuvant systemic breast cancer therapy

BACKGROUND

Mammographic density (MD) is a risk factor for breast cancer (BC) development, and recurrence. However, its predictive value has been less studied. Herein, we challenged MD as a biomarker associated with response in patients treated with neoadjuvant therapy (NAT).

METHODS

Data on all NAT treated BC patients prospectively collected in the registry of Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy (2009-2019) were identified. Diagnostic mammograms were used to evaluate and score MD as categorized by the Breast Imaging-Reporting and Data System (BI-RADS), which identifies 4 levels of MD in keeping with relative increase of fibro-glandular over fat tissue. Each case was classified according to the following categories a (MD < 25%), b (26-50%), c (51-75%), and d (> 75%). The association between MD and pathological complete response (pCR), i.e., absence of BC cells in surgical specimens, was analyzed in multivariable setting used logistic regression models with adjustment for clinical and pathological variables.

RESULTS

A total of 442 patients were analyzed, 120 of which (27.1%) attained a pCR. BI-RADS categories a, b, c, and d accounted for 10.0%, 37.8%, 37.1% and 15.2% of cases. Corresponding pCR were 20.5%, 26.9%, 30.5%, 23.9%, respectively. At multivariable analysis, when compared to cases classified as BI-RADS a, those with denser breast showed an increased likelihood of pCR with odds ratio (OR) of 1.70, 2.79, and 1.47 for b, c and d categories, respectively (p = 0.0996), independently of age, BMI [OR underweight versus (vs) normal = 3.76], clinical nodal and tumor status (OR T1/Tx vs T4 = 3.87), molecular subtype (HER2-positive vs luminal = 10.74; triple-negative vs luminal = 8.19). In subgroup analyses, the association of MD with pCR was remarkable in triple-negative (ORs of b, c and d versus a: 1.85, 2.49 and 1.55, respectively) and HER2-positive BC cases (ORs 2.70, 3.23, and 1.16).

CONCLUSION

Patients with dense breast are more likely to attain a pCR at net of other predictive factors. The potential of MD to assist decisions on BC management and as a stratification factor in neoadjuvant clinical trials should be considered.

Preparation and bioevaluation of [99mTc]Tc-labeled A7R and DA7R for SPECT imaging of triple-negative breast cancer

[99mTc]Tc-labeled D-type A7R peptide showed better tumor-to-muscle ratios and lower renal uptake.

Postoperative Adjuvant Imatinib Therapy-Associated Nomogram to Predict Overall Survival of Gastrointestinal Stromal Tumor

BACKGROUND

Adjuvant imatinib therapy has been shown to improve overall survival (OS) of gastrointestinal stromal tumor (GIST) significantly. Few nomograms combining the use of adjuvant imatinib and clinicopathological characteristics estimate the outcome of patients. We aimed to establish a more comprehensive nomogram for predicting OS in patients with GIST.

METHODS

In total, 1310 GIST patients undergoing curative resection at four high-volume medical centers between 2001 and 2015 were enrolled. Independent prognostic factors were identified by multivariate Cox analysis. Eligible patients were randomly assigned in a ratio of 7:3 into a training set (916 cases) and a validation set (394 cases). A nomogram was established by R software and its predictive power compared with that of the modified National Institutes of Health (NIH) classification using time-dependent receiver operating characteristic (ROC) curves and calibration plot.

RESULTS

Age, tumor site, tumor size, mitotic index, postoperative imatinib and diagnostic delay were identified as independent prognostic parameters and used to construct a nomogram. Of note, diagnostic delay was for the first time included in a prognostic model for GIST. The calibrated nomogram resulted in predicted survival rates consistent with observed ones. And the decision curve analysis suggested that the nomogram prognostic model was clinically useful. Furthermore, time-dependent ROC curves showed the nomogram exhibited greater discrimination power than the modified NIH classification in 3- and 5-year survival predictions for both training and validation sets (all P < 0.05).

CONCLUSIONS

Postoperative adjuvant imatinib therapy improved the survival of GIST patients. We developed and validated a more comprehensive prognostic nomogram for GIST patients, and it could have important clinical utility in improving individualized predictions of survival risks and treatment decision-making.

Racial Disparities in Diagnostic Delay Among Women With Breast Cancer

PURPOSE

Early diagnosis is fundamental to reducing breast cancer (BC) mortality, and understanding potential barriers from initial screening to confirmed diagnosis is essential. The aim of this study was to evaluate patient characteristics that contribute to delay in diagnosis of screen-detected cancers and the contribution of delay to tumor characteristics and BC mortality.

METHODS

Three hundred sixty-two White and 368 Black women were identified who were screened and received subsequent BC diagnoses within Emory Healthcare, a part of Emory University health care system (2010-2014). Multivariable-adjusted logistic regression was used to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) associating patient characteristics with delay to diagnostic evaluation (≥30 versus <30 days), delay to biopsy (≥15 versus <15 days), and total delay (≥45 versus <45 days). Additionally, the ORs and 95% CIs associating delay with tumor characteristics and BC mortality were computed.

RESULTS

Black women and women diagnosed at later stages, with larger tumor sizes, and with triple-negative tumors were more likely to experience ≥45 days to diagnosis. In multivariable-adjusted models, Black women had at least a two-fold increase in the odds of delay to diagnostic evaluation (OR, 1.98; 95% CI, 1.45-2.71), biopsy delays (OR, 2.41; 95% CI, 1.67-3.41), and total delays ≥45 days (OR, 2.22; 95% CI, 1.63-3.02) compared with White women. A 1.6-fold increased odds of BC mortality was observed among women who experienced total delays ≥45 days compared with women without delays in diagnosis (OR, 1.57, 95% CI, 0.96-2.58).

CONCLUSIONS

The study demonstrated racial disparities in delays in the diagnostic process for screen-detected malignancies. Total delay in diagnosis was associated with an increase in BC mortality.

Which type of cancer is detected in breast screening programs? Review of the literature with focus on the most frequent histological features

Breast cancer is the most frequent type of cancer affecting female patients. The introduction of breast cancer screening programs led to a substantial reduction of mortality from breast cancer. Nevertheless, doubts are being raised on the real efficacy of breast screening programs. The aim of the present paper is to review the main pathological type of cancers detected in breast cancer screening programs. Specifically, attention will be given to: in situ carcinoma, invasive carcinoma histotypes and interval cancer.

Clinical Implications of Androgen-Positive Triple-Negative Breast Cancer

This review summarizes the recent findings of a vast array of studies conducted on androgen receptor-positive triple-negative breast cancer (AR-positive TNBC) to provide a better understanding of this specific breast cancer subgroup. AR expression is correlated with higher age, lower histological grade, lower proliferation index Ki-67, spiculated masses, and calcifications on mammography. Studies investigating the correlation between AR expression and lymph node metastasis are highly discordant. In addition, results regarding prognosis are highly contradictory. AR antagonists are a promising novel therapeutic approach in AR-positive TNBC. However, AR signaling pathways should be more investigated in order to understand the influence of AR expression on TNBC more thoroughly.

Triple Negative Breast Cancer Detection Using LC-MS/MS Lipidomic Profiling

Breast cancer (BC) is a heterogeneous malignancy that is responsible for a great portion of female cancer cases and cancer-related deaths in the United States. In comparison to other major BC subtypes, triple negative breast cancer (TNBC) presents with a relatively low survival rate and a high rate of metastasis. This has led to a strong, though largely unmet, need for more sensitive and specific methods of early-stage TNBC (ES-TNBC) detection to combat its high-grade pathology and relatively low survival rate. The current study employs a liquid chromatography-tandem mass spectrometry assay capable of targeted, highly specific, and sensitive detection of lipids to propose two diagnostic biomarker panels for TNBC/ES-TNBC. Using this approach, 110 lipids were reliably detected in 166 human plasma samples, 45 controls, and 121 BC (96 non-TNBC and 25 TNBC) subjects. Univariate and multivariate analyses allowed the construction and application of a 19-lipid biomarker panel capable of distinguishing TNBC (and ES-TNBC) from controls, as well as a 5-lipid biomarker panel capable of differentiating TNBC from non-TNBC and ES-TNBC from ES-non-TNBC. Receiver operating characteristic curves with notable classification performances were generated from the biomarker panels according to their orthogonal partial least-squares discrimination analysis models. TNBC was distinguished from controls with an area under the receiving operating characteristic curve (AUROC) = 0.93, sensitivity = 0.96, and specificity = 0.76 and ES-TNBC from controls with an AUROC = 0.96, sensitivity = 0.95, and specificity = 0.89. TNBC was differentiated from non-TNBC with an AUROC = 0.88, sensitivity = 0.88, and specificity = 0.79 and ES-TNBC from ES-non-TNBC with an AUROC = 0.95, sensitivity = 0.95, and specificity = 0.87. A pathway enrichment analysis between TNBC and controls also revealed significant disturbances in choline metabolism, sphingolipid signaling, and glycerophospholipid metabolism. To the best of our knowledge, this is the first study to propose a diagnostic lipid biomarker panel for TNBC detection. All raw mass spectrometry data have been deposited to MassIVE (dataset identifier MSV000085324).