A diagnostic gene profile for molecular subtyping of breast cancer associated with treatment response

Neo-adjuvant therapies for ER positive/HER2 negative breast cancers: from chemotherapy to hormonal therapy, CDK inhibitors, and beyond

INTRODUCTION

Chemotherapy has been traditionally used as neo-adjuvant therapy in breast cancer for down-staging of locally advanced disease in all sub-types. In the adjuvant setting, genomic assays have shown that a significant proportion of ER positive/HER2 negative patients do not derive benefit from the addition of chemotherapy to adjuvant endocrine therapy. An interest in hormonal treatments as neo-adjuvant therapies in ER positive/HER2 negative cancers has been borne by their documented success in the adjuvant setting. Moreover, cytotoxic chemotherapy is less effective in ER positive/HER2 negative disease compared with other breast cancer subtypes in obtaining pathologic complete responses.

AREAS COVERED

Neo-adjuvant therapies for ER positive/HER2 negative breast cancers and associated biomarkers are reviewed, using a Medline survey. A focus of discussion is the prediction of patients that are unlikely to derive extra benefit from chemotherapy and have the highest probabilities of benefiting from hormonal and other targeted therapies.

EXPERT OPINION

Predictive biomarkers of response to neo-adjuvant chemotherapy and hormonal therapies are instrumental for selecting ER positive/HER2 negative breast cancer patients for each treatment. Chemotherapy remains the standard of care for many of those patients requiring neo-adjuvant treatment, but other neo-adjuvant therapies are increasingly used.

Prediction of Benefit From Adjuvant Pertuzumab by 80-Gene Signature in the APHINITY (BIG 4-11) Trial

PURPOSE

At the primary analysis, the APHINITY trial reported a statistically significant but modest benefit of adding pertuzumab to standard adjuvant chemotherapy plus trastuzumab in patients with histologically confirmed human epidermal growth factor receptor 2 (HER2)-positive early-stage breast cancer. This study evaluated whether the 80-gene molecular subtyping signature (80-GS) could identify patients within the APHINITY population who derive the most benefit from dual anti-HER2 therapy.

METHODS

In a nested case-control study design of 1,023 patients (matched event to control ratio of 3:1), the 80-GS classified breast tumors into functional luminal type, HER2 type, or basal type. Additionally, 80-GS distinguished tumor subtypes that exhibited a single-dominant functional pathway versus tumors with multiple activated pathways. The primary end point was invasive disease-free survival (IDFS). Hazard ratios (HRs) were evaluated by Cox regression. After excluding patients without appropriate consent and those with missing data, 964 patients were included.

RESULTS

The 80-GS classified 50% (n = 479) of tumors as luminal type, 28% (n = 275) as HER2 type, and 22% (n = 209) as basal type. Most luminal-type tumors (86%) displayed a single-activated pathway, whereas 49% of HER2-type and 42% of basal-type tumors were dual activated. There was no significant difference in IDFS among different conventional 80-GS subtypes (single- and dual-activated subtypes combined). However, basal single-subtype tumors were significantly more likely to have an IDFS event (hazard ratio, 1.69 [95% CI, 1.12 to 2.54]) compared with other subtypes. HER2 single-subtype tumors displayed a trend toward greater beneficial effect on the addition of pertuzumab (hazard ratio, 0.56 [95% CI, 0.27 to 1.16]) compared with all other subtypes.

CONCLUSION

The 80-GS identified subgroups of histologically confirmed HER2-positive tumors with distinct biological characteristics. Basal single-subtype tumors exhibit an inferior prognosis compared with other subgroups and may be candidates for additional therapeutic strategies. Preliminary results suggest patients with HER2-positive, genomically HER2 single-subtype tumors may particularly benefit from added pertuzumab, which warrants further investigation.

The Predictive Utility of MammaPrint and BluePrint in Identifying Patients with Locally Advanced Breast Cancer Who are Most Likely to Have Nodal Downstaging and a Pathologic Complete Response After Neoadjuvant Chemotherapy

BACKGROUND

Neoadjuvant chemotherapy (NCT) increases the feasibility of surgical resection by downstaging large primary breast tumors and nodal involvement, which may result in surgical de-escalation and improved outcomes. This subanalysis from the Multi-Institutional Neo-adjuvant Therapy MammaPrint Project I (MINT) trial evaluated the association between MammaPrint and BluePrint with nodal downstaging.

PATIENTS AND METHODS

The prospective MINT trial (NCT01501487) enrolled 387 patients between 2011 and 2016 aged ≥ 18 years with invasive breast cancer (T2-T4). This subanalysis includes 146 patients with stage II-III, lymph node positive, who received NCT. MammaPrint stratifies tumors as having a Low Risk or High Risk of distant metastasis. Together with MammaPrint, BluePrint genomically (g) categorizes tumors as gLuminal A, gLuminal B, gHER2, or gBasal.

RESULTS

Overall, 45.2% (n = 66/146) of patients had complete nodal downstaging, of whom 60.6% (n = 40/66) achieved a pathologic complete response. MammaPrint and combined MammaPrint and BluePrint were significantly associated with nodal downstaging (p = 0.007 and p < 0.001, respectively). A greater proportion of patients with MammaPrint High Risk tumors had nodal downstaging compared with Low Risk (p = 0.007). When classified with MammaPrint and BluePrint, more patients with gLuminal B, gHER2, and gBasal tumors had nodal downstaging compared with HR+HER2-, gLuminal A tumors (p = 0.538, p < 0.001, and p = 0.013, respectively).

CONCLUSIONS

Patients with genomically High Risk tumors, defined by MammaPrint with or without BluePrint, respond better to NCT and have a higher likelihood of nodal downstaging compared with patients with gLuminal A tumors. These genomic signatures can be used to select node-positive patients who are more likely to have nodal downstaging and avoid invasive surgical procedures.

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.

Invasive Breast Cancer with HER2 ≥4.0 and <6.0: Risk Classification and Molecular Typing by a 21-Gene Expression Assay and MammaPrint Plus BluePrint Testing

PURPOSE

To investigate the HER2 status and clinicopathological features in invasive breast cancer with HER2 ≥4.0 and <6.0, which has always been controversial.

METHODS

Forty breast cancer cases with HER2 ≥4.0 and <6.0 by fluorescence in situ hybridization (FISH) were collected and classified into two groups based on the HRE2/CEP17 ratio (Group A: ≥2.0, n=22; Group B: <2.0, n=18). Clinicopathological characteristics, HER2 status, risk classification, and molecular typing were further analyzed and compared by 21-Gene expression assay and MammaPrint plus BluePrint test.

RESULTS

The majority of cases in both groups were invasive carcinoma (NOS), with histological grade II, HR+, Ki-67 ≥20%, HER2 2+, and a high risk of recurrence, although younger patients and lymph node metastases were more common in Group A. Surprisingly, all HR+ breast cancers in both groups were classified as luminal-type, HR- cases were all basal-type or unknown, and the index of HER2 in all cases was <0.000 using the BluePrint test, which indicated that HER2 status should be negative. Furthermore, the level of HER2 mRNA expression in all cases of both groups was <10.7, which was defined as HER2 negative by the 21-Gene expression assay. In addition, 10 patients of Group A received anti-HER2 neoadjuvant therapy; only one patient with HR- achieved Grade 5 based on the Miller-Payne system, whereas none of the patients achieved pathological complete response (pCR) based on the Residual Cancer Burden system.

CONCLUSION

Group A breast cancer, which has always been unquestionably diagnosed as HER2 amplification, was more likely to be HER2 negative and derived less benefit from anti-HER2 neoadjuvant chemotherapy. Group A breast cancer should be distinguished from classical HER2-positive breast cancers when assessing HER2 FISH, and a larger cohort of Group A patients should be included in further studies.

Imagine beyond: recent breakthroughs and next challenges in mammary gland biology and breast cancer research

On 8 December 2022 the organizing committee of the European Network for Breast Development and Cancer labs (ENBDC) held its fifth annual Think Tank meeting in Amsterdam, the Netherlands. Here, we embraced the opportunity to look back to identify the most prominent breakthroughs of the past ten years and to reflect on the main challenges that lie ahead for our field in the years to come. The outcomes of these discussions are presented in this position paper, in the hope that it will serve as a summary of the current state of affairs in mammary gland biology and breast cancer research for early career researchers and other newcomers in the field, and as inspiration for scientists and clinicians to move the field forward.

BluePrint molecular subtypes predict response to neoadjuvant pertuzumab in HER2-positive breast cancer

BACKGROUND

The introduction of pertuzumab has greatly improved pathological complete response (pCR) rates in HER2-positive breast cancer, yet effects on long-term survival have been limited and it is uncertain which patients derive most benefit. In this study, we determine the prognostic value of BluePrint subtyping in HER2-positive breast cancer. Additionally, we evaluate its use as a biomarker for predicting response to trastuzumab-containing neoadjuvant chemotherapy with or without pertuzumab.

METHODS

From a cohort of patients with stage II-III HER2-positive breast cancer who were treated with neoadjuvant chemotherapy and trastuzumab with or without pertuzumab, 836 patients were selected for microarray gene expression analysis, followed by readout of BluePrint standard (HER2, Basal and Luminal) and dual subtypes (HER2-single, Basal-single, Luminal-single, HER2-Basal, Luminal-HER2, Luminal-HER2-Basal). The associations between subtypes and pathological complete response (pCR), overall survival (OS) and breast cancer-specific survival (BCSS) were assessed, and pertuzumab benefit was evaluated within the BluePrint subgroups.

RESULTS

BluePrint results were available for 719 patients. In patients with HER2-type tumors, the pCR rate was 71.9% in patients who received pertuzumab versus 43.5% in patients who did not (adjusted Odds Ratio 3.43, 95% CI 2.36-4.96). Additionally, a significantly decreased hazard was observed for both OS (adjusted hazard ratio [aHR] 0.45, 95% CI 0.25-0.80) and BCSS (aHR 0.46, 95% CI 0.24-0.86) with pertuzumab treatment. Findings were similar in the HER2-single subgroup. No significant benefit of pertuzumab was seen in other subtypes.

CONCLUSIONS

In patients with HER2-type or HER2-single-type tumors, pertuzumab significantly improved the pCR rate and decreased the risk of breast cancer mortality, which was not observed in other subtypes. BluePrint subtyping may be valuable in future studies to identify patients that are likely to be highly sensitive to HER2-targeting agents.

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

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

Role of DEK in carcinogenesis, diagnosis, prognosis, and therapeutic outcome of breast cancer: An evidence-based clinical review

Breast cancer is a significantly burdening women's cancer with limited diagnostic modalities. DEK is a novel biomarker overexpressed in breast cancers, currently exhaustively researched for its diagnosis and prognosis. Search for relevant meta-analyses, cohorts, and experimental studies in the last fifteen years was done in five large scientific databases. Non-English, non-full text articles or unrelated studies were excluded. Thirteen articles discussed the potential of DEK to estimate breast cancer characteristics, treatment outcomes, and prognosis. This proto-oncogene plays a role in breast carcinogenesis, increasing tumour proliferation and invasion, preventing apoptosis, and creating an immunodeficient tumour milieu with M2 tumour-associated macrophages. DEK is also associated with worse clinicopathological features and survival in breast cancer patients. Using a Kaplan-Meier plotter data analysis, DEK expression predicts worse overall survival (HR 1.24, 95%CI: 1.01-1.52, p = 0.039), comparable to other biomarkers. DEK is a promising novel biomarker requiring further research to determine its bedside applications.

Twenty-Year Benefit From Adjuvant Goserelin and Tamoxifen in Premenopausal Patients With Breast Cancer in a Controlled Randomized Clinical Trial

PURPOSE

To assess the long-term (20-year) endocrine therapy benefit in premenopausal patients with breast cancer.

METHODS

Secondary analysis of the Stockholm trial (STO-5, 1990-1997) randomly assigning 924 premenopausal patients to 2 years of goserelin (3.6 mg subcutaneously once every 28 days), tamoxifen (40 mg orally once daily), combined goserelin and tamoxifen, or no adjuvant endocrine therapy (control) is performed. Random assignment was stratified by lymph node status; lymph node-positive patients (n = 459) were allocated to standard chemotherapy (cyclophosphamide, methotrexate, and fluorouracil). Primary tumor immunohistochemistry (n = 731) and gene expression profiling (n = 586) were conducted in 2020. The 70-gene signature identified genomic low-risk and high-risk patients. Kaplan-Meier analysis, multivariable Cox proportional hazard regression, and multivariable time-varying flexible parametric modeling assessed the long-term distant recurrence-free interval (DRFI). Swedish high-quality registries allowed a complete follow-up of 20 years.

RESULTS

In estrogen receptor-positive patients (n = 584, median age 47 years), goserelin, tamoxifen, and the combination significantly improved long-term distant recurrence-free interval compared with control (multivariable hazard ratio [HR], 0.49; 95% CI, 0.32 to 0.75, HR, 0.57; 95% CI, 0.38 to 0.87, and HR, 0.63; 95% CI, 0.42 to 0.94, respectively). Significant goserelin-tamoxifen interaction was observed (P = .016). Genomic low-risk patients (n = 305) significantly benefitted from tamoxifen (HR, 0.24; 95% CI, 0.10 to 0.60), and genomic high-risk patients (n = 158) from goserelin (HR, 0.24; 95% CI, 0.10 to 0.54). Increased risk from the addition of tamoxifen to goserelin was seen in genomic high-risk patients (HR, 3.36; 95% CI, 1.39 to 8.07). Moreover, long-lasting 20-year tamoxifen benefit was seen in genomic low-risk patients, whereas genomic high-risk patients had early goserelin benefit.

CONCLUSION

This study shows 20-year benefit from 2 years of adjuvant endocrine therapy in estrogen receptor-positive premenopausal patients and suggests differential treatment benefit on the basis of tumor genomic characteristics. Combined goserelin and tamoxifen therapy showed no benefit over single treatment. Long-term follow-up to assess treatment benefit is critical.

Combined 70- and 80-gene signatures identify tumors with genomically luminal biology responsive to neoadjuvant endocrine therapy and are prognostic of 5-year outcome in early-stage breast cancer

BACKGROUND

As more patients with early-stage breast cancer receive neoadjuvant endocrine therapy (NET), there is a need for reliable biomarkers that can identify patients with HR+ HER2- tumors who are likely to benefit from NET. NBRST (NCT01479101) compared the prognostic value of the 70-gene risk classification and 80-gene molecular subtyping signatures with conventional pathological classification methods in response to neoadjuvant therapy. We evaluated the association of these signatures with clinical response and 5-year outcome of patients treated with NET.

METHODS

1091 patients with early-stage breast cancer scheduled to receive neoadjuvant therapy were prospectively enrolled into NBRST, and a sub-analysis of 67 patients treated with NET was performed. Patients received standard of care genomic testing using the 70-gene and 80-gene signatures and were treated with NET, per physician's discretion. The primary endpoint was pathologic partial response (pPR) and secondary endpoints were distant metastasis-free survival (DMFS) and overall survival (OS). Clinical benefit was defined as having a pPR or stable disease (SD) with NET.

RESULTS

Overall, 94.4% of patients with genomically (g) Luminal A-Type (50.0% pPR and 44.4% SD) and 95.0% with Luminal B-Type tumors (55.0% pPR and 40.0% SD) exhibited clinical benefit. At 5 years, patients with gLuminal B tumors had significantly worse DMFS (75.6%, 95% CI 50.8-89.1) than patients with gLuminal A (91.1%; 95% CI 74.8-97.1; p = 0.047), with a similar trend for OS, albeit not significant (81.0%, 95% CI 56.9-92.4 and 91.1%, 95% CI 74.8-97.1, respectively; p = 0.13).

CONCLUSIONS

Genomic assays offer a broader understanding of the underlying tumor biology, which adds precision to pathology as a preoperative risk classifier. Patients with 70-gene signature Low Risk, gLuminal A tumors treated with endocrine therapy alone have excellent 5-year outcomes. Most patients with genomically-defined Luminal A- and B-Type tumors respond well to NET, suggesting these patients may be safely treated with NET, while those with gLuminal B tumors will also require post-operative chemotherapy or CDK4/6 inhibitors to improve long-term outcomes. Overall, these findings demonstrate that genomic classification, defined by the combined 70- and 80-gene signatures, is associated with tumor response and prognostic of long-term outcomes.

Association between Obesity, Race or Ethnicity, and Luminal Subtypes of Breast Cancer

Luminal breast cancers are the most common genomic subtype of breast cancers where Luminal A cancers have a better prognosis than Luminal B. Exposure to sex steroids and inflammatory status due to obesity are key contributors of Luminal tumor development. In this study, 1928 patients with Luminal A breast cancer and 1610 patients with Luminal B breast cancer were compared based on body mass index (BMI), age, race, menopausal status, and expressed receptors (i.e., estrogen (ER), progesterone (PR), and human epidermal growth factor receptor 2 (HER2)). Patients with Luminal B tumors had a significantly higher mean BMI (Δ = 0.69 kgm−2 [0.17, 1.21], p = 0.010) versus Luminal A. Interestingly, the risks of Luminal B tumors were higher among Black/African American patients versus White and Hispanic patients (p < 0.001 and p = 0.001, respectively). When controlled for each other, Black/African American race (p < 0.001) and increased BMI (p = 0.008) were associated with increased risks of Luminal B carcinoma, while postmenopausal status was associated with a decreased risk (p = 0.028). Increased BMI partially mediated the strong association between Black/African American race and the risk of Luminal B carcinoma. Thus, Black/African American race along with obesity seem to be associated with an increased risk of more aggressive Luminal B breast carcinomas.

Genomic Classification of HER2-Positive Patients With 80-Gene and 70-Gene Signatures Identifies Diversity in Clinical Outcomes With HER2-Targeted Neoadjuvant Therapy

The prospective Neoadjuvant Breast Registry Symphony Trial compared the 80-gene molecular subtyping signature with clinical assessment by immunohistochemistry and/or fluorescence in situ hybridization in predicting pathologic complete response (pCR) and 5-year outcomes in patients with early-stage breast cancer.

BluePrint breast cancer molecular subtyping recognizes single and dual subtype tumors with implications for therapeutic guidance

Purpose

BluePrint (BP) is an 80-gene molecular subtyping test that classifies early-stage breast cancer (EBC) into Basal, Luminal, and HER2 subtypes. In most cases, breast tumors have one dominant subtype, representative of a single activated pathway. However, some tumors show a statistically equal representation of more than one subtype, referred to as dual subtype. This study aims to identify and examine dual subtype tumors by BP to understand their biology and possible implications for treatment guidance.

Methods

The BP scores of over 15,000 tumor samples from EBC patients were analyzed, and the differences between the highest and the lowest scoring subtypes were calculated. Based upon the distribution of the differences between BP scores, a threshold was determined for each subtype to identify dual versus single subtypes.

Results

Approximately 97% of samples had one single activated BluePrint molecular subtype, whereas ~ 3% of samples were classified as BP dual subtype. The most frequently occurring dual subtypes were the Luminal-Basal-type and Luminal-HER2-type. Luminal-Basal-type displays a distinct biology from the Luminal single type and Basal single type. Burstein’s classification of the single and dual Basal samples showed that the Luminal-Basal-type is mostly classified as ‘luminal androgen receptor’ and ‘mesenchymal’ subtypes, supporting molecular evidence of AR activation in the Luminal-Basal-type tumors. Tumors classified as Luminal-HER2-type resemble features of both Luminal-single-type and HER2-single-type. However, patients with dual Luminal-HER2-type have a lower pathological complete response after receiving HER2-targeted therapies in addition to chemotherapy in comparison with patients with a HER2-single-type.

Conclusion

This study demonstrates that BP identifies tumors with two active functional pathways (dual subtype) with specific transcriptional characteristics and highlights the added value of distinguishing BP dual from single subtypes as evidenced by distinct treatment response rates.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10549-022-06698-x.

HER2-low breast cancers: incidence, HER2 staining patterns, clinicopathologic features, MammaPrint and BluePrint genomic profiles

Recently, clinical trials have demonstrated promising efficacy for novel HER2-targeted therapies in HER2-low breast cancers, raising the prospect of including a HER2-low category (immunohistochemical [IHC] score of 1+, or 2+ with non-amplified in-situ hybridization [ISH]) in the HER2 evaluation of breast cancers. In order to better understand this newly-proposed HER2 category, we investigated the incidence, HER2 staining patterns, clinicopathologic features, and genomic profile of HER2-low breast cancers. HER2-stained slides of 281 consecutive breast cancers were re-reviewed and the clinicopathologic information, MammaPrint, and BluePrint results of these cases were retrospectively analyzed. HER2-low breast cancers were identified in 31% of cases and were more common in estrogen receptor (ER)-positive than ER-negative breast cancers (33.6% vs 15%, p = 0.017). HER2-low cancers were generally clinical stages I-II (79%), ER-positive (93.1%), had homogenous HER2 staining (59.2%), HER2 IHC score of 1+ (87.4%), ductal phenotype (81.6%), histologic grades of 1 or 2 (94.2%) and luminal molecular subtypes (94.3%). Three HER2-low patients received neoadjuvant chemotherapy and none of them achieved pathologic complete response. When compared to HER2-negative (IHC of 0+) and HER2-positive (IHC of 3+ or IHC of 2+ with amplified ISH) cancers, HER2-low breast cancers had significantly lower Ki-67 (p = 0.03 and p < 0.01, respectively) and higher ER positivity (p = 0.01 and p = 0.03, respectively). HER2-low breast cancers were less likely to be basal molecular subtype when compared to HER2-negative cancers (p < 0.01) and were less likely to have a HER2 molecular subtype when compared to HER2-positive cancers (p < 0.01). When adjusted for ER status, there was no significant difference on all the examined variables between HER2-low and HER2-negative groups. Our study provides valuable baseline characteristics of HER2-low breast cancers deriving from consecutive, real-world cases with a consensus confirmation of HER2 status, and would help to increase our understanding of this newly-proposed HER2 category in breast cancers.

Breast cancer in the era of precision medicine

Breast cancer is a heterogeneous disorder with different molecular subtypes and biological characteristics for which there are diverse therapeutic approaches and clinical outcomes specific to any molecular subtype. It is a global health concern due to a lack of efficient therapy regimens that might be used for all disease subtypes. Therefore, treatment customization for each patient depending on molecular characteristics should be considered. Precision medicine for breast cancer is an approach to diagnosis, treatment, and prevention of the disease that takes into consideration the patient's genetic makeup. Precision medicine provides the promise of highly individualized treatment, in which each individual breast cancer patient receives the most appropriate diagnostics and targeted therapies based on the genetic profile of cancer. The knowledge about the molecular features and development of breast cancer treatment approaches has increased, which led to the development of new targeted therapeutics. Tumor genomic profiling is the standard of care for breast cancer that could contribute to taking steps to better management of malignancies. It holds great promise for accurate prognostication, prediction of response to common systemic therapies, and individualized monitoring of the disease. The emergence of targeted treatment has significantly enhanced the survival of patients with breast cancer and contributed to reducing the economic costs of the health system. In this review, we summarized the therapeutic approaches associated with the molecular classification of breast cancer to help the best treatment selection specific to the target patient.

Redefining breast cancer subtypes to guide treatment prioritization and maximize response: Predictive biomarkers across 10 cancer therapies

Using pre-treatment gene expression, protein/phosphoprotein, and clinical data from the I-SPY2 neoadjuvant platform trial (NCT01042379), we create alternative breast cancer subtypes incorporating tumor biology beyond clinical hormone receptor (HR) and human epidermal growth factor receptor-2 (HER2) status to better predict drug responses. We assess the predictive performance of mechanism-of-action biomarkers from ∼990 patients treated with 10 regimens targeting diverse biology. We explore >11 subtyping schemas and identify treatment-subtype pairs maximizing the pathologic complete response (pCR) rate over the population. The best performing schemas incorporate Immune, DNA repair, and HER2/Luminal phenotypes. Subsequent treatment allocation increases the overall pCR rate to 63% from 51% using HR/HER2-based treatment selection. pCR gains from reclassification and improved patient selection are highest in HR+ subsets (>15%). As new treatments are introduced, the subtyping schema determines the minimum response needed to show efficacy. This data platform provides an unprecedented resource and supports the usage of response-based subtypes to guide future treatment prioritization.

Limiting systemic endocrine overtreatment in postmenopausal breast cancer patients with an ultralow classification of the 70-gene signature

Purpose

Guidelines recommend endocrine treatment for estrogen receptor-positive (ER+) breast cancers for up to 10 years. Earlier data suggest that the 70-gene signature (MammaPrint) has potential to select patients that have an excellent survival without chemotherapy and limited or no tamoxifen treatment. The aim was to validate the 70-gene signature ultralow-risk classification for endocrine therapy decision making.

Methods

In the IKA trial, postmenopausal patients with non-metastatic breast cancer had been randomized between no or limited adjuvant tamoxifen treatment without receiving chemotherapy. For this secondary analysis, FFPE tumor material was obtained of ER+HER2− patients with 0–3 positive lymph nodes and tested for the 70-gene signature. Distant recurrence-free interval (DRFI) long-term follow-up data were collected. Kaplan–Meier curves were used to estimate DRFI, stratified by lymph node status, for the three predefined 70-gene signature risk groups.

Results

A reliable 70-gene signature could be obtained for 135 patients. Of the node-negative and node-positive patients, respectively, 20% and 13% had an ultralow-risk classification. No DRFI events were observed for node-negative patients with an ultralow-risk score in the first 10 years. The 10-year DRFI was 90% and 66% in the low-risk (but not ultralow) and high-risk classified node-negative patients, respectively.

Conclusion

These survival analyses indicate that the postmenopausal node-negative ER+HER2− patients with an ultralow-risk 70-gene signature score have an excellent 10-year DRFI after surgery with a median of 1 year of endocrine treatment. This is in line with published results of the STO-3-randomized clinical trial and supports the concept that it is possible to reduce the duration of endocrine treatment in selected patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10549-022-06618-z.

Distinct Neoadjuvant Chemotherapy Response and 5-Year Outcome in Patients With Estrogen Receptor-Positive, Human Epidermal Growth Factor Receptor 2-Negative Breast Tumors That Reclassify as Basal-Type by the 80-Gene Signature

The 80-gene molecular subtyping signature (80-GS) reclassifies a proportion of immunohistochemistry (IHC)-defined luminal breast cancers (estrogen receptor–positive [ER+], human epidermal growth factor receptor 2–negative [HER2–]) as Basal-Type. We report the association of 80-GS reclassification with neoadjuvant treatment response and 5-year outcome in patients with breast cancer.

Identity exposed: genomic assay unmasks TNBC-like breast cancer tumors disguised as HR+ #NBRST

Discovery of genes positively modulating treatment effect using potential outcome framework and Bayesian update

Background

The recent explosion of cancer genomics provides extensive information about mutations and gene expression changes in cancer. However, most of the identified gene mutations are not clinically utilized. It remains uncertain whether the presence of a certain genetic alteration will affect treatment response. Conventional statistics have limitations for causal inferences and are hard to gain sufficient power in genomic datasets. Here, we developed and evaluated a C-search algorithm for searching the causal genes that maximize the effect of the treatment.

Methods

The algorithm was developed based on the potential outcome framework and Bayesian posterior update. The precision of the algorithm was validated using a simulation dataset. The algorithm was implemented to a cBioPortal dataset. The genes discovered by the algorithm were externally validated within CancerSCAN screening data from Samsung Medical Center.

Results

Simulation data analysis showed that the C-search algorithm was able to identify nine causal genes out of ten. The C-search algorithm shows the discovery rate rapidly increasing until the 1500 data instances. Meanwhile, the log-rank test shows a slower increase in performance. The C-search algorithm was able to suggest nine causal genes from the cBioPortal Metabric dataset. Treating the patients with the causal genes is associated with better survival outcome in both the cBioPortal dataset and the CancerSCAN dataset which is used for external validation.

Conclusions

Our C-search algorithm demonstrated better performance to identify causal effects of the genes than multiple log-rank test analysis especially within a limited number of data. The result suggests that the C-search can discover the causal genes from various genetic datasets, where the number of samples is limited compared to the number of variables.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01852-3.

Treatment response and 5-year distant metastasis-free survival outcome in breast cancer patients after the use of MammaPrint and BluePrint to guide preoperative systemic treatment decisions

AIM

In the prospective neoadjuvant NBREaST II study, we measured the response to preoperative treatment and 5-year survival outcome in the molecular subgroups as determined by combining the MammaPrint and BluePrint.

METHODS

Between 2012 and 2016 we included 256 patients for whom MammaPrint and BluePrint were performed on pre-treatment core needle biopsies. The primary objective of the NBREaST II trial was to measure chemosensitivity or endocrine sensitivity in the molecular subgroups. Distant metastasis-free survival (DMFS), relapse-free survival (RFS) and breast cancer-specific survival (BCSS) were the endpoints for long-term follow-up.

RESULTS

MammaPrint and BluePrint molecular sub-typing reclassified 9% (24/256) of tumours, reassigning more responsive patients to the HER2-Type and Basal-Type, and less responsive patients to the Luminal-Type category. Patients with Luminal A-Type tumours (n = 67, 26% of the total cohort) had a poor response when treated with neoadjuvant chemotherapy (NCT), but had the highest 5-year DMFS outcome (91.4%; 95% CI 78.6-96.7) of all molecular subgroups. Out of the IHC/FISH defined HER2+ tumours (n = 41), 37% were not classified as HER2-Type by BluePrint. Notably, in BluePrint HER2-Type tumours, we observed a higher pCR rate, whereas the 5-year DMFS was lower compared to IHC/FISH-defined HER2+ tumours. The pCR rate and 5-year outcome for patients with Basal-Type tumours were similar to IHC/FISH-defined TN tumours.

CONCLUSION

These findings suggest that MammaPrint and BluePrint can predict chemosensitivity and 5-year outcomes more accurately compared to traditional pathological sub-typing, supporting informed decision-making.

Age-Independent Preoperative Chemosensitivity and 5-Year Outcome Determined by Combined 70- and 80-Gene Signature in a Prospective Trial in Early-Stage Breast Cancer

BACKGROUND

The Neoadjuvant Breast Symphony Trial (NBRST) demonstrated the 70-gene risk of distant recurrence signature, MammaPrint, and the 80-gene molecular subtyping signature, BluePrint, precisely determined preoperative pathological complete response (pCR) in breast cancer patients. We report 5-year follow-up results in addition to an exploratory analysis by age and menopausal status.

METHODS

The observational, prospective NBRST (NCT01479101) included 954 early-stage breast cancer patients aged 18-90 years who received neoadjuvant chemotherapy and had clinical and genomic data available. Chemosensitivity and 5-year distant metastasis-free survival (DMFS) and overall survival (OS) were assessed. In a post hoc subanalysis, results were stratified by age (≤ 50 vs. > 50 years) and menopausal status in patients with hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR+/HER2-) tumors.

RESULTS

MammaPrint and BluePrint further classified 23% of tumors to a different subtype compared with immunohistochemistry, with more precise correspondence to pCR rates. Five-year DMFS and OS were highest in MammaPrint Low Risk, Luminal A-type and HER2-type tumors, and lowest in MammaPrint High Risk, Luminal B-type and Basal-type tumors. There was no significant difference in chemosensitivity between younger and older patients with Low-Risk (2.2% vs. 3.8%; p = 0.64) or High-Risk tumors (14.5% vs. 11.5%; p = 0.42), or within each BluePrint subtype; this was similar when stratifying by menopausal status. The 5-year outcomes were comparable by age or menopausal status for each molecular subtype.

CONCLUSION

Intrinsic preoperative chemosensitivity and long-term outcomes were precisely determined by BluePrint and MammaPrint regardless of patient age, supporting the utility of these assays to inform treatment and surgical decisions in early-stage breast cancer.

Axillary Response to Neoadjuvant Therapy in Node-Positive, Estrogen Receptor-Positive, Human Epidermal Growth Factor Receptor 2-Negative Breast Cancer Patients: Predictors and Oncologic Outcomes

BACKGROUND

One potential benefit of neoadjuvant therapy (NAT) in node-positive, estrogen receptor-positive (ER+), human epidermal growth factor receptor 2-negative (HER2-) patients is axillary downstaging to avoid axillary dissection.

OBJECTIVE

The aim of this study was to evaluate axillary response to NAT with chemotherapy (NCT) or endocrine therapy (NET) and identify potential predictors of response.

METHODS

A prospectively collected database was queried for node-positive, ER+, HER2- breast cancer patients treated with NAT and surgery from January 2011 to September 2020. Axillary response was categorized into pathologic complete response (pCR) versus no pCR, and was correlated to demographic and clinicopathologic parameters in a logistic regression model.

RESULTS

A cohort of 176 eligible patients was identified and 178 breast cancers were included in the study. The overall axillary pCR rate was 12.3% (22/178). NCT and NET achieved response rates of 13.9% (19/137) and 7.3% (3/41), respectively (p = 0.232). A significantly higher axillary pCR rate was identified in patients with clinical stage II at diagnosis (12/60, 20%) compared with stage III (10/118, 8.4%; p = 0.03). NET patients with ypN0 were younger and were treated for a longer period of time (>6 months). Completion axillary dissection was omitted in the majority (73.7%) of NCT patients achieving axillary pCR.

CONCLUSIONS

For patients with node-positive, ER+, HER2- breast cancer, a lower burden of disease at the time of diagnosis (stage II) is associated with a significantly higher axillary pCR, enabling those patients to be spared axillary dissection. Further studies are necessary to define the role of genomic profiling in predicting axillary response.

The clinical impacts of molecular subtyping by multigene assay on hormone receptor-positive breast cancers

BACKGROUND

Multigene assays, such as MammaPrint and BluePrint, provide additional information other than conventional immunohistochemistry (IHC) to help making decision of treatment. This study aims to compare the clinical correlation between molecular subtyping (MS) versus surrogate pathological subtyping (PS).

METHODS

A database from patients receiving MS evaluation in Taipei Veterans General Hospital from 2013 to 2018 was reviewed retrospectively. Patients were categorized as luminal A, luminal B, human epidermal growth factor receptor 2 (HER2) and basal type from MS results and also centrally assessed according to PS (estrogen receptor [ER], progesterone receptor [PgR], HER2, and Ki-67). The clinical correlation between two different subtyping methodologies was analyzed, and the application of chemotherapy was compared.

RESULTS

From 2013 to 2018, a total of 130 patients received MS testing in our institute, and 132 tumor samples were sent for analysis. From MammaPrint, 64 (48.5%) and 55 (41.7%) samples were defined as low and high risks, respectively. The other 13 (9.8%) tumor samples were identified as late recurrence low risk. MS restratified 44 tumors as subtype shifting including 20 tumors from A to B in intrinsic subtypes and 24 tumors from B to A after MS evaluation. Chemotherapy was conducted in only one (1.3%) patient with MS-luminal A but in 87.8% (n = 43) of MS-luminal B subtypes.

CONCLUSION

The MS results restratify the subtypes of hormone receptor positive breast cancer and dominate decision-making of adjuvant therapy. The role of surrogate biomarkers as an alternative tool needs further elucidation. The treatment outcome in different subtypes categorized by MS or PS will be the interesting focus of research.

HER2+ Breast Cancer Escalation and De-Escalation Trial Design: Potential Role of Intrinsic Subtyping

Simple Summary

Classical clinical research has been developed according to immunohistochemical breast cancer subtypes, instead of designing trials specifically for each molecular subtype. Efforts in de-escalating treatment should focus on identifying a subgroup of HER2 oncogene addicted tumours that are especially sensitive to anti-HER2 therapies and, thus, spare unnecessary treatments. A prognostic assay that integrates molecular tumour features with clinical and pathologic variables and accurately defines a group of HER2 addicted tumours remains the best candidate among these strategies.

Abstract

Long-term outcomes in breast cancer patients differ based on the molecular subtype, with HER2-E being the most aggressive one. Advances in clinical practice have dramatically shifted HER2+ breast cancer prognosis. Risk adapted strategies to individualize therapies are necessary. De-escalation approaches have been encouraged based on the risks of clinical-pathological factors. Molecular gene subtyping could further accurately define HER2 addicted tumours that are sensitive to anti-HER2 therapies, thus sparing unnecessary treatments. The transition from immunochemistry to molecular profiling in HER2+ breast cancer is discussed.

Investigating Deep Learning Based Breast Cancer Subtyping Using Pan-Cancer and Multi-Omic Data

Breast Cancer comprises multiple subtypes implicated in prognosis. Existing stratification methods rely on the expression quantification of small gene sets. Next Generation Sequencing promises large amounts of omic data in the next years. In this scenario, we explore the potential of machine learning and, particularly, deep learning for breast cancer subtyping. Due to the paucity of publicly available data, we leverage on pan-cancer and non-cancer data to design semi-supervised settings. We make use of multi-omic data, including microRNA expressions and copy number alterations, and we provide an in-depth investigation of several supervised and semi-supervised architectures. Obtained accuracy results show simpler models to perform at least as well as the deep semi-supervised approaches on our task over gene expression data. When multi-omic data types are combined together, performance of deep models shows little (if any) improvement in accuracy, indicating the need for further analysis on larger datasets of multi-omic data as and when they become available. From a biological perspective, our linear model mostly confirms known gene-subtype annotations. Conversely, deep approaches model non-linear relationships, which is reflected in a more varied and still unexplored set of representative omic features that may prove useful for breast cancer subtyping.

High concordance of 70-gene recurrence risk signature and 80-gene molecular subtyping signature between core needle biopsy and surgical resection specimens in early-stage breast cancer

BACKGROUND AND OBJECTIVES

With increased neoadjuvant therapy recommendations for early-stage breast cancer patients due to the COVID-19 pandemic, it is imperative that molecular diagnostic assays provide reliable results from preoperative core needle biopsies (CNB). The study objective was to determine the concordance of MammaPrint and BluePrint results between matched CNB and surgical resection (SR) specimens.

METHODS

Matched tumor specimens (n = 121) were prospectively collected from women enrolled in the FLEX trial (NCT03053193). Concordance is reported using overall percentage agreement and Cohen's kappa coefficient. Correlation is reported using Pearson correlation coefficient.

RESULTS

We found good concordance for MammaPrint results between matched tumor samples (90.9%, κ = 0.817), and a very strong correlation of MammaPrint indices (r = 0.94). The concordance of BluePrint subtyping in matched samples was also excellent (98.3%).

CONCLUSIONS

CNB samples demonstrated high concordance with paired SR samples for MammaPrint risk classification and BluePrint molecular subtyping, suggesting that physicians are provided with accurate prognostic information that can be used to guide therapy decisions.

Gene-expression signatures to inform neoadjuvant treatment decision in HR+/HER2- breast cancer: Available evidence and clinical implications

Over the last few years, the indication for chemotherapy use in HR+/HER2- early BC has been significantly modified by the introduction of gene-expression profiling. In the adjuvant setting, several gene-expression signatures have been validated to discriminate early stage HR+/HER2- BC with different prognosis and to identify patients for which adjuvant chemotherapy can be spared. Considering their ability to optimize the choice of adjuvant treatment and the increasing use of neoadjuvant approach in early BC, the potential use of gene-expression signatures to discriminate patients to be candidate to neoadjuvant chemotherapy or endocrine treatment appears particularly appealing. Indeed, the San Gallen Consensus Conference panel recently endorsed the use of genomic assays on core biopsies as a potential strategy for choosing the type of neoadjuvant treatment (chemotherapy or endocrine therapy) in selected patients. In this context, we here review evidence supporting the use of most common commercially available gene-expression signatures (Oncotype DX, MammaPrint, PAM50, EndoPredict and Breast Cancer Index) in patients receiving neoadjuvant therapy for HR+/HER2- BC. Data on the association of gene expression signatures and response to neoadjuvant chemotherapy or neoadjuvant endocrine therapy will be reviewed and the clinical implications of this data to guide the clinical decision-making process in early HR+/HER2- BC will be discussed.

Predicting Breast Cancer Gene Expression Signature by Applying Deep Convolutional Neural Networks From Unannotated Pathological Images

We proposed a highly versatile two-step transfer learning pipeline for predicting the gene signature defining the intrinsic breast cancer subtypes using unannotated pathological images. Deciphering breast cancer molecular subtypes by deep learning approaches could provide a convenient and efficient method for the diagnosis of breast cancer patients. It could reduce costs associated with transcriptional profiling and subtyping discrepancy between IHC assays and mRNA expression. Four pretrained models such as VGG16, ResNet50, ResNet101, and Xception were trained with our in-house pathological images from breast cancer patient with recurrent status in the first transfer learning step and TCGA-BRCA dataset for the second transfer learning step. Furthermore, we also trained ResNet101 model with weight from ImageNet for comparison to the aforementioned models. The two-step deep learning models showed promising classification results of the four breast cancer intrinsic subtypes with accuracy ranging from 0.68 (ResNet50) to 0.78 (ResNet101) in both validation and testing sets. Additionally, the overall accuracy of slide-wise prediction showed even higher average accuracy of 0.913 with ResNet101 model. The micro- and macro-average area under the curve (AUC) for these models ranged from 0.88 (ResNet50) to 0.94 (ResNet101), whereas ResNet101_imgnet weighted with ImageNet archived an AUC of 0.92. We also show the deep learning model prediction performance is significantly improved relatively to the common Genefu tool for breast cancer classification. Our study demonstrated the capability of deep learning models to classify breast cancer intrinsic subtypes without the region of interest annotation, which will facilitate the clinical applicability of the proposed models.

MammaPrint and BluePrint comprehensively capture the cancer hallmarks in early-stage breast cancer patients

MammaPrint® (MP) is a 70‐gene signature that stratifies early‐stage breast cancer patients into low‐ and high risk of distant relapse. Further stratification of MP risk results identifies four risk subgroups, ultra‐low (UL), low, high 1, and high 2, with specific prognostic and predictive outcomes. BluePrint® (BP) is an 80‐gene signature that classifies breast tumors as basal, luminal, or HER2 molecular subtype. To gain insight into their biological significance, we annotated the MP 70‐ and BP 80‐genes with respect to the 10 hallmarks of cancer (HoC). Furthermore, we related gene expression profiles of the extreme ends of the MP low‐ and high‐risk patients (here called, ultra‐low (UL) and ultra‐high (UH) or High2, respectively), to the 10 HoC per BP subtype by differential gene expression and pathway analysis. MP and BP gene functions reflected all 10 HoCs. Most MP and BP genes were associated with sustaining proliferative signaling, followed by genome instability and mutation categories. Based on the gene expression profiles, UL and UH subgroup pathways were down ‐or upregulated, respectively, reflecting proliferative and metastatic features, such as G2M checkpoint, DNA repair, oxidative phosphorylation, immune invasion, PI3K/AKT/mTOR signaling, and hypoxia pathways. Notably, the UH HER2‐type was enriched in several immune signaling pathways, such as IL2/STAT5 signaling and TNFα signaling via NFκB. Our results show that MP and BP gene signatures represent and capture all 10 HoCs and highlight underlying biological processes of MP extreme samples, which might guide treatment decisions as the signature captures the full spectrum of early breast cancers.

Prediction of Breast Cancer Recurrence Using a Deep Convolutional Neural Network Without Region-of-Interest Labeling

Purpose

The present study aimed to assign a risk score for breast cancer recurrence based on pathological whole slide images (WSIs) using a deep learning model.

Methods

A total of 233 WSIs from 138 breast cancer patients were assigned either a low-risk or a high-risk score based on a 70-gene signature. These images were processed into patches of 512x512 pixels by the PyHIST tool and underwent color normalization using the Macenko method. Afterward, out of focus and pixelated patches were removed using the Laplacian algorithm. Finally, the remaining patches (n=294,562) were split into 3 parts for model training (50%), validation (7%) and testing (43%). We used 6 pretrained models for transfer learning and evaluated their performance using accuracy, precision, recall, F1 score, confusion matrix, and AUC. Additionally, to demonstrate the robustness of the final model and its generalization capacity, the testing set was used for model evaluation. Finally, the GRAD-CAM algorithm was used for model visualization.

Results

Six models, namely VGG16, ResNet50, ResNet101, Inception_ResNet, EfficientB5, and Xception, achieved high performance in the validation set with an overall accuracy of 0.84, 0.85, 0.83, 0.84, 0.87, and 0.91, respectively. We selected Xception for assessment of the testing set, and this model achieved an overall accuracy of 0.87 with a patch-wise approach and 0.90 and 1.00 with a patient-wise approach for high-risk and low-risk groups, respectively.

Conclusions

Our study demonstrated the feasibility and high performance of artificial intelligence models trained without region-of-interest labeling for predicting cancer recurrence based on a 70-gene signature risk score.

Molecular subtyping of breast cancer intrinsic taxonomy with oligonucleotide microarray and NanoString nCounter

Breast cancer intrinsic subtypes have been identified based on the transcription of a predefined gene expression (GE) profiles and algorithm (PAM50). This study compared molecular subtyping with oligonucleotide microarray and NanoString nCounter assay. A total of 109 Taiwanese breast cancers (24 with adjacent normal breast tissues) were assayed with Affymetrix Human Genome U133 plus 2.0 microarrays and 144 were assayed with the NanoString nCounter while 64 patients were assayed for both platforms. Subtyping with the nearest centroid (single sample prediction) was performed, and 16 out of 24 (67%) matched normal breasts were categorized as the normal breast-like subtype. For 64 breast cancers assayed for both platforms, 41 (65%, one unclassified by microarray) were predicted with an identical subtype, resulting in a fair Kappa statistic of 0.60. Taking nCounter subtyping as the gold standard, prediction accuracy was 43% (3/7), 81% (13/16), 25% (5/20), and 100% (20/20) for basal-like, HER2-enriched, luminal A and luminal B subtype predicted from microarray GE profiles. Microarray identified more luminal B cases from luminal A subtype predicted by nCounter. It's not uncommon to use microarray for breast cancer molecular subtyping for research. Our study showed that fundamental discrepancy existed between distinct GE assays, and cross platform equivalence should be carefully appraised when molecular subtyping was conducted with oligonucleotide microarray.

High-performance deep learning pipeline predicts individuals in mixtures of DNA using sequencing data

In this study, we proposed a deep learning (DL) model for classifying individuals from mixtures of DNA samples using 27 short tandem repeats and 94 single nucleotide polymorphisms obtained through massively parallel sequencing protocol. The model was trained/tested/validated with sequenced data from 6 individuals and then evaluated using mixtures from forensic DNA samples. The model successfully identified both the major and the minor contributors with 100% accuracy for 90 DNA mixtures, that were manually prepared by mixing sequence reads of 3 individuals at different ratios. Furthermore, the model identified 100% of the major contributors and 50-80% of the minor contributors in 20 two-sample external-mixed-samples at ratios of 1:39 and 1:9, respectively. To further demonstrate the versatility and applicability of the pipeline, we tested it on whole exome sequence data to classify subtypes of 20 breast cancer patients and achieved an area under curve of 0.85. Overall, we present, for the first time, a complete pipeline, including sequencing data processing steps and DL steps, that is applicable across different NGS platforms. We also introduced a sliding window approach, to overcome the sequence length variation problem of sequencing data, and demonstrate that it improves the model performance dramatically.

Phase II Trial of Neoadjuvant Carboplatin and Nab-Paclitaxel in Patients with Triple-Negative Breast Cancer

BACKGROUND

In this phase II clinical trial, we evaluated the efficacy of the nonanthracycline combination of carboplatin and nab-paclitaxel in early stage triple-negative breast cancer (TNBC).

PATIENTS AND METHODS

Patients with newly diagnosed stage II-III TNBC (n = 69) were treated with neoadjuvant carboplatin (area under the curve 6) every 28 days for four cycles plus nab-paclitaxel (100 mg/m2 ) weekly for 16 weeks. Pathological complete response (pCR) and residual cancer burden (RCB) were analyzed with germline mutation status, tumor-infiltrating lymphocytes (TILs), TNBC molecular subtype, and GeparSixto immune signature (GSIS).

RESULTS

Sixty-seven patients were evaluable for safety and response. Fifty-three (79%) patients experienced grade 3/4 adverse events, including grade 3 anemia (43%), neutropenia (39%), leukopenia (15%), thrombocytopenia (12%), fatigue (7%), peripheral neuropathy (7%), neutropenia (16%), and leukopenia (1%). Twenty-four patients (35%) had at least one dose delay, and 50 patients (72%) required dose reduction. Sixty-three (94%) patients completed scheduled treatment. The responses were as follows: 32 of 67 patients (48%) had pCR (RCB 0), 10 of 67 (15%) had RCB I, 19 of 67 (28%) had RCB II, 5 of 67 (7%) had RCB III, and 1 of 67 (2%) progressed and had no surgery. Univariate analysis showed that immune-hot GSIS and DNA repair defect (DRD) were associated with higher pCR with odds ratios of 4.62 (p = .005) and 4.76 (p = .03), respectively, and with RCB 0/I versus RCB II/III with odds ratio 4.80 (p = .01). Immune-hot GSIS was highly correlated with DRD status (p = .03), TIL level (p < .001), and TNBC molecular subtype (p < .001). After adjusting for age, race, stage, and grade, GSIS remained associated with higher pCR and RCB class 0/I versus II/III with odds ratios 7.19 (95% confidence interval [CI], 2.01-25.68; p = .002) and 8.95 (95% CI, 2.09-38.23; p = .003), respectively.

CONCLUSION

The combination of carboplatin and nab-paclitaxel for early stage high-risk TNBC showed manageable toxicity and encouraging antitumor activity. Immune-hot GSIS is associated with higher pCR rate and RCB class 0/1. This study provides an additional rationale for using nonanthracycline platinum-based therapy for future neoadjuvant trials in early stage TNBCs. Clinical trial identification number: NCT01525966 IMPLICATIONS FOR PRACTICE: Platinum is an important neoadjuvant chemotherapy agent for treatment of early stage triple-negative breast cancer (TNBC). In this study, carboplatin and nab-paclitaxel were well tolerated and highly effective in TNBC, resulting in pathological complete response of 48%. In univariate and multivariate analyses adjusting for age, race, tumor stage and grade, "immune-hot" GeparSixto immune signature (GSIS) and DNA repair defect (DRD) were associated with higher pathological complete response (pCR) and residual cancer burden class 0/1. The association of immune-hot GSIS with higher pCR holds promise for de-escalating neoadjuvant chemotherapy for patients with early stage TNBC. Although GSIS is not routinely used in clinic, further development of this immune signature into a clinically applicable assay is indicated.

Genomic Assays in Node Positive Breast Cancer Patients: A Review

In recent years, developments in breast cancer have allowed yet another realization of individualized medicine in the field of oncology. One of these advances is genomic assays, which are considered elements of standard clinical practice in the management of breast cancer. These assays are widely used today not only to measure recurrence risk in breast cancer patients at an early stage but also to tailor treatment as well and minimize avoidable treatment side effects. At present, genomic tests are applied extensively in node negative disease. In this article, we review the use of these tests in node positive disease, explore their ramifications on neoadjuvant chemotherapy decisions, highlight sufficiently powered recent studies emphasizing their use and review the most recent guidelines.

Breast cancer molecular subtypes and receptor status among women at Potchefstroom Hospital: a cross-sectional study

Introduction

this study aimed to determine the prevalence of receptor status and molecular subtypes in women with breast cancer treated at Potchefstroom Regional Hospital, South Africa and to analyze the association of molecular subtypes with some clinicopathologic characteristics of the tumor.

Methods

the study population for this cross-sectional study consisted of 116 women with primary invasive breast cancer, treated at the hospital from 1^st January 2012 to 31^st December 2018. Molecular subtypes were classified by immunohistochemical surrogates as luminal A (estrogen receptor (ER) positive and/or progesterone receptor (PR) positive, HER2-; Ki-67 <30%), luminal B HER2- (ER+ and/or PR+, HER2-; Ki-67 ≥30%), luminal B HER2+ (ER+ and/or PR+, HER2+; any Ki-67), HER2 enriched (ER- and PR-, HER2+; any Ki-67), or triple-negative (ER-, PR-, HER2-, any Ki-67).

Results

the proportions of breast cancer receptor status of ER+, PR+ and HER2-, were 71.6%, 64.7% and 75.9%, respectively. The molecular subtypes of 29.3% of patients were luminal A-type, 24.1% were luminal B HER2-, 22.4% were triple-negative, 18.1% were luminal B HER2+ and 6% were HER2-enriched. Molecular subtypes were significantly associated with tumor grade (p <0.001; Cramér's V=0.337), but independent of age (p=0.847), menopausal status (p=0.690), histology type (p=0.316), cancer stage (p=0.819), lymph node status (p=0.362), or tumor size (p=0.255).

Conclusion

the study has revealed that most of the breast cancer in our setting was receptor-positive; approximately one-quarter were triple-negative. Furthermore, the study showed that luminal type A and B are the preponderant molecular subtypes. Molecular subtypes were associated with tumor grade but independent of age and menopausal status. The current study may assist in guiding the therapeutic strategy for patients with breast cancer in the Potchefstroom hospital catchment area.

Magee Equations™ and response to neoadjuvant chemotherapy in ER+/HER2-negative breast cancer: a multi-institutional study

Magee Equations™ (ME) are multivariable models that can estimate oncotype DX^® recurrence score. One of the equations, Magee Equation 3 (ME3) which utilizes only semi-quantitative receptor results has been shown to provide chemopredictive value in the neoadjuvant setting in a single institutional study. This multi-institutional study (seven institutions contributed cases) was undertaken to examine the validity of ME3 in predicting response to neoadjuvant chemotherapy in estrogen receptor positive, HER2-negative breast cancers. Stage IV cases were excluded. The primary endpoint was the pathologic complete response (pCR) rate in different categories of ME3 scores calculated based on receptor results in the pre-therapy core biopsy. A total of 166 cases met the inclusion criteria. The patient age ranged from 24 to 83 years (median 53 years). The average pre-therapy tumor size was 3.9 cm, and axillary lymph nodes were confirmed positive by pre-therapy core biopsy in 85 of 166 cases (51%). The pCR rate according to ME3 scores was 0% (0 of 64) in ME3 < 18, 0% (0 of 46) in ME3 18-25, 14% (3 of 21) in ME3 > 25 to <31, and 40% (14 of 35) in ME3 score 31 or higher (p value: <0.0001). There were no distant recurrences and no deaths in the 17 patients with pCR. In the remaining 149 cases with residual disease, ME3 score of >25 was significantly associated with shorter distant recurrence-free survival and showed a trend for shorter breast cancer-specific survival. The results of this multi-institutional study are similar to previously published data from a single institution (PMID: 28548119) and confirm the chemo-predictive value of ME3 in the neoadjuvant setting. In addition, ME3 may provide prognostic information in patients with residual disease which should be further evaluated.

Wnt signaling in breast cancer: biological mechanisms, challenges and opportunities

Wnt signaling is a highly conserved signaling pathway that plays a critical role in controlling embryonic and organ development, as well as cancer progression. Genome-wide sequencing and gene expression profile analyses have demonstrated that Wnt signaling is involved mainly in the processes of breast cancer proliferation and metastasis. The most recent studies have indicated that Wnt signaling is also crucial in breast cancer immune microenvironment regulation, stemness maintenance, therapeutic resistance, phenotype shaping, etc. Wnt/β-Catenin, Wnt-planar cell polarity (PCP), and Wnt-Ca2+ signaling are three well-established Wnt signaling pathways that share overlapping components and play different roles in breast cancer progression. In this review, we summarize the main findings concerning the relationship between Wnt signaling and breast cancer and provide an overview of existing mechanisms, challenges, and potential opportunities for advancing the therapy and diagnosis of breast cancer.

Prognostic factors in patients with oligometastatic breast cancer - A systematic review

AIM

Oligometastatic breast cancer (OMBC) is a disease-entity with potential for long-term remission in selected patients. Those with truly limited metastatic load (rather than occult widespread metastatic disease) may benefit from multimodality treatment including local ablative therapy of distant metastases. In this systematic review, we studied factors associated with long-term survival in patients with OMBC.

METHODS

Eligible studies included patients with OMBC who received a combination of local and systemic therapy as multimodal approach and reported overall survival (OS) or progression-free survival (PFS), or both. The Quality in Prognosis Studies (QUIPS) tool was used to assess the quality of each included study. Independent prognostic factors for OS and/or PFS are summarized.

RESULTS

Of 1271 screened abstracts, 317 papers were full-text screened and twenty studies were included. Eleven of twenty studies were classified as acceptable quality. Definition of OMBC varied between studies and mostly incorporated the number and/or location of metastases. The 5-year OS ranged between 30 and 79% and 5-year PFS ranged between 25 and 57%. Twelve studies evaluated prognostic factors for OS and/or PFS in multivariable models. A solitary metastasis, >24 months interval between primary tumor and OMBC, no or limited involved axillary lymph nodes at primary diagnosis, and hormone-receptor positivity were associated with better outcome. HER2-positivity was associated with worse outcome, but only few patients received anti-HER2 therapy.

CONCLUSIONS

OMBC patients with a solitary distant metastasis and >24 months disease-free interval have the best OS and may be optimal candidates to consider a multidisciplinary approach.

Genomic Signatures in Luminal Breast Cancer

Background

Breast cancer is a very heterogeneous disease and luminal breast carcinomas represent the hormone receptor-positive tumors among all breast cancer subtypes. In this context, multigene signatures were developed to gain further prognostic and predictive information beyond clinical parameters and traditional immunohistochemical markers.

Summary

For early breast cancer patients these molecular tools can guide clinicians to decide on the extension of endocrine therapy to avoid over- and undertreatment by adjuvant chemotherapy. Beside the predictive and prognostic value, a few genomic tests are also able to provide intrinsic subtype classification. In this review, we compare the most frequently used and commercially available molecular tests (OncotypeDX®, MammaPrint®, Prosigna®, EndoPredict®, and Breast Cancer Index^SM). Moreover, we discuss the clinical utility of molecular profiling for advanced breast cancer of the luminal subtype.

Key Messages

Multigene assays can help to de-escalate systemic therapy in early-stage breast cancer. Only the Oncotype DX® and MammaPrint®test are validated by entirely prospective and randomized phase 3 trials. More clinical evidence is needed to support the use of genomic tests in node-positive disease. Recent developments in high-throughput sequencing technology will provide further insights to understand the heterogeneity of luminal breast cancers in early-stage and metastatic disease.

Secreted breast tumor interstitial fluid microRNAs and their target genes are associated with triple-negative breast cancer, tumor grade, and immune infiltration

Background

Studies on tumor-secreted microRNAs point to a functional role of these in cellular communication and reprogramming of the tumor microenvironment. Uptake of tumor-secreted microRNAs by neighboring cells may result in the silencing of mRNA targets and, in turn, modulation of the transcriptome. Studying miRNAs externalized from tumors could improve cancer patient diagnosis and disease monitoring and help to pinpoint which miRNA-gene interactions are central for tumor properties such as invasiveness and metastasis.

Methods

Using a bioinformatics approach, we analyzed the profiles of secreted tumor and normal interstitial fluid (IF) microRNAs, from women with breast cancer (BC). We carried out differential abundance analysis (DAA), to obtain miRNAs, which were enriched or depleted in IFs, from patients with different clinical traits. Subsequently, miRNA family enrichment analysis was performed to assess whether any families were over-represented in the specific sets. We identified dysregulated genes in tumor tissues from the same cohort of patients and constructed weighted gene co-expression networks, to extract sets of co-expressed genes and co-abundant miRNAs. Lastly, we integrated miRNAs and mRNAs to obtain interaction networks and supported our findings using prediction tools and cancer gene databases.

Results

Network analysis showed co-expressed genes and miRNA regulators, associated with tumor lymphocyte infiltration. All of the genes were involved in immune system processes, and many had previously been associated with cancer immunity. A subset of these, BTLA, CXCL13, IL7R, LAMP3, and LTB, was linked to the presence of tertiary lymphoid structures and high endothelial venules within tumors. Co-abundant tumor interstitial fluid miRNAs within this network, including miR-146a and miR-494, were annotated as negative regulators of immune-stimulatory responses. One co-expression network encompassed differences between BC subtypes. Genes differentially co-expressed between luminal B and triple-negative breast cancer (TNBC) were connected with sphingolipid metabolism and predicted to be co-regulated by miR-23a. Co-expressed genes and TIF miRNAs associated with tumor grade were BTRC, CHST1, miR-10a/b, miR-107, miR-301a, and miR-454.

Conclusion

Integration of IF miRNAs and mRNAs unveiled networks associated with patient clinicopathological traits, and underlined molecular mechanisms, specific to BC sub-groups. Our results highlight the benefits of an integrative approach to biomarker discovery, placing secreted miRNAs within a biological context.

SurvBoost: An R Package for High-Dimensional Variable Selection in the Stratified Proportional Hazards Model via Gradient Boosting

High-dimensional variable selection in the proportional hazards (PH) model has many successful applications in different areas. In practice, data may involve confounding variables that do not satisfy the PH assumption, in which case the stratified proportional hazards (SPH) model can be adopted to control the confounding effects by stratification without directly modeling the confounding effects. However, there is a lack of computationally efficient statistical software for high-dimensional variable selection in the SPH model. In this work an R package, SurvBoost, is developed to implement the gradient boosting algorithm for fitting the SPH model with high-dimensional covariate variables. Simulation studies demonstrate that in many scenarios SurvBoost can achieve better selection accuracy and reduce computational time substantially compared to the existing R package that implements boosting algorithms without stratification. The proposed R package is also illustrated by an analysis of gene expression data with survival outcome in The Cancer Genome Atlas study. In addition, a detailed hands-on tutorial for SurvBoost is provided.

Performance Characteristics of the BluePrint® Breast Cancer Diagnostic Test

The analytical performance of a multi-gene diagnostic signature depends on many parameters, including precision, repeatability, reproducibility and intra-tumor heterogeneity. Here we study the analytical performance of the BluePrint 80-gene breast cancer molecular subtyping test through determination of these performance characteristics. BluePrint measures the expression of 80 genes that assess functional pathways which determine the intrinsic breast cancer molecular subtypes (i.e. Luminal-type, HER2-type, Basal-type). Knowing a tumor's dominant functional pathway can help allocate effective treatment to appropriate patients. Here we show that BluePrint is a highly precise and highly reproducible test with correlations above 98% based on the generated index and subtype concordance above 99%. Therefore, BluePrint can be used as a robust and reliable tool to identify breast cancer molecular subtypes.

The therapeutic response of ER+/HER2- breast cancers differs according to the molecular Basal or Luminal subtype

The genomics-based molecular classifications aim at identifying more homogeneous classes than immunohistochemistry, associated with a more uniform clinical outcome. We conducted an in silico analysis on a meta-dataset including gene expression data from 5342 clinically defined ER+/HER2- breast cancers (BC) and DNA copy number/mutational and proteomic data. We show that the Basal (16%) versus Luminal (74%) subtypes as defined using the 80-gene signature differ in terms of response/vulnerability to systemic therapies of BC. The Basal subtype is associated with better chemosensitivity, lesser benefit from adjuvant hormone therapy, and likely better sensitivity to PARP inhibitors, platinum salts and immune therapy, and other targeted therapies under development such as FGFR inhibitors. The Luminal subtype displays potential better sensitivity to CDK4/6 inhibitors and vulnerability to targeted therapies such as PIK3CA, AR and Bcl-2 inhibitors. Expression profiles are very different, showing an intermediate position of the ER+/HER2- Basal subtype between the ER+/HER2- Luminal and ER- Basal subtypes, and let suggest a different cell-of-origin. Our data suggest that the ER+/HER2- Basal and Luminal subtypes should not be assimilated and treated as a homogeneous group.

MammaPrint guides treatment decisions in breast Cancer: results of the IMPACt trial

Background

Increased usage of genomic risk assessment assays suggests increased reliance on data provided by these assays to guide therapy decisions. The current study aimed to assess the change in treatment decision and physician confidence based on the 70-gene risk of recurrence signature (70-GS, MammaPrint) and the 80-gene molecular subtype signature (80-GS, BluePrint) in early stage breast cancer patients.

Methods

IMPACt, a prospective, case-only study, enrolled 452 patients between November 2015 and August 2017. The primary objective population included 358 patients with stage I-II, hormone receptor-positive, HER2-negative breast cancer. The recommended treatment plan and physician confidence were captured before and after receiving results for 70-GS and 80-GS. Treatment was started after obtaining results. The distribution of 70-GS High Risk (HR) and Low Risk (LR) patients was evaluated, in addition to the distribution of 80-GS compared to IHC status.

Results

The 70-GS classified 62.5% (n = 224/358) of patients as LR and 37.5% (n = 134/358) as HR. Treatment decisions were changed for 24.0% (n = 86/358) of patients after receiving 70-GS and 80-GS results. Of the LR patients initially prescribed CT, 71.0% (44/62) had CT removed from their treatment recommendation. Of the HR patients not initially prescribed CT, 65.1% (41/63) had CT added. After receiving 70-GS results, CT was included in 83.6% (n = 112/134) of 70-GS HR patient treatment plans, and 91.5% (n = 205/224) of 70-GS LR patient treatment plans did not include CT. For patients who disagreed with the treatment recommended by their physicians, most (94.1%, n = 16/17) elected not to receive CT when it was recommended. For patients whose physician-recommended treatment plan was discordant with 70-GS results, discordance was significantly associated with age and lymph node status.

Conclusions

The IMPACt trial showed that treatment plans were 88.5% (n = 317/358) in agreement with 70-GS results, indicating that physicians make treatment decisions in clinical practice based on the 70-GS result. In clinically high risk, 70-GS Low Risk patients, there was a 60.0% reduction in treatment recommendations that include CT. Additionally, physicians reported having greater confidence in treatment decisions for their patients in 72% (n = 258/358) of cases after receiving 70-GS results.

Trial registration

“Measuring the Impact of MammaPrint on Adjuvant and Neoadjuvant Treatment in Breast Cancer Patients: A Prospective Registry” (NCT02670577) retrospectively registered on Jan 27, 2016.

CAIX is a predictor of pathological complete response and is associated with higher survival in locally advanced breast cancer submitted to neoadjuvant chemotherapy

BACKGROUND

Locally advanced breast cancer often undergoes neoadjuvant chemotherapy (NAC), which allows in vivo evaluation of the therapeutic response. The determination of the pathological complete response (pCR) is one way to evaluate the response to neoadjuvant chemotherapy. However, the rate of pCR differs significantly between molecular subtypes and the cause is not yet determined. Recently, the metabolic reprogramming of cancer cells and its implications for tumor growth and dissemination has gained increasing prominence and could contribute to a better understanding of NAC. Thus, this study proposed to evaluate the expression of metabolism-related proteins and its association with pCR and survival rates.

METHODS

The expression of monocarboxylate transporters 1 and 4 (MCT1 and MCT4, respectively), cluster of differentiation 147 (CD147), glucose transporter-1 (GLUT1) and carbonic anhydrase IX (CAIX) was analyzed in 196 locally advanced breast cancer samples prior to NAC. The results were associated with clinical-pathological characteristics, occurrence of pCR, disease-free survival (DFS), disease-specific survival (DSS) and overall survival (OS).

RESULTS

The occurrence of pCR was higher in the group of patients whith tumors expressing GLUT1 and CAIX than in the group without expression (27.8% versus 13.1%, p = 0.030 and 46.2% versus 13.5%, p = 0.007, respectively). Together with regional lymph nodes staging and mitotic staging, CAIX expression was considered an independent predictor of pCR. In addition, CAIX expression was associated with DFS and DSS (p = 0.005 and p = 0.012, respectively).

CONCLUSIONS

CAIX expression was a predictor of pCR and was associated with higher DFS and DSS in locally advanced breast cancer patients subjected to NAC.