Phenotypic alterations in breast cancer associated with neoadjuvant chemotherapy: A comparison with baseline rates of change

Hariri, Nosaibah; Roma, Andres A.; Hasteh, Farnaz; Walavalkar, Vighnesh; Fadare, Oluwole

doi:10.1016/j.anndiagpath.2017.06.004

Cited by 6 publications

(3 citation statements)

References 56 publications

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“…With the popularization of NACT in locally advanced breast cancer, we have compiled the results over the past ten years (Table 1 ) and found that, compared with the biomarkers in samples obtained by fine needle aspiration or hollow needle biopsy before surgery, postoperative tissue receptors can occasionally produce completely opposite conclusions. The average conversion rate of ER is 7.3%, PR is 15.0% while HER2 is only 6.8% which is consistent with previous data, that is, the status of PR is most inclined to change while HER2 is relatively more stable[ 3 ].…”

Section: Discussionsupporting

confidence: 90%

HER2 changes to positive after neoadjuvant chemotherapy in breast cancer: A case report and literature review

Luo¹,

Jiang²,

He³

et al. 2022

WJCC

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BACKGROUND As the most common cancer in women, breast cancer is the leading cause of death. Most patients are initially diagnosed as stage I-III. Among those without distant metastases, 64% are local tumors and 27% are regional tumors. Patients in stage IIA-IIIC and those who meet the breast-conserving criterion with the exception of tumor size can consider neoadjuvant chemotherapy (NACT). It is worth noting that the status of tumor cell biomarkers is not consistently static. Endocrine-related estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) encoded by erythroblastic leukemia viral oncogene homolog 2 gene can all alter from positive to negative or vice versa , especially in luminal B subtype after NACT. In addition, determination of HER2 status currently mainly relies on immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH), but FISH is commonly used when the result of IHC is uncertain. HER2 is regarded as negative when the IHC result is 0/1+ without the addition of FISH. To the best of our knowledge, this is the first report of a case harboring HER2 status transformation and IHC1+ with positive amplification by FISH after NACT. CASE SUMMARY A 49-year-old woman discovered a mass in her right breast and underwent diagnostic workup. Biopsies of the right breast lesion and axillary lymph nodes were obtained. The results pointed to invasive ductal carcinoma with the IHC result for ER (80%), PR (60%), Ki-67 (20%) and ambiguous expression of HER2 (IHC 2+) with negative amplification by FISH (HER2/CEP17 ratio of 1.13). She underwent surgery after NACT. The pathological findings of the surgically resected sample supported invasive ductal carcinoma with the tumor measuring 1.1 cm × 0.8 cm × 0.5 cm and had spread to one of fifteen dissected lymph nodes. Retesting of the specimen showed that the tumor was positive for ER (2+, 85%) and PR (2+, 10%) but negative for HER2 by IHC (1+). Also Ki-67 had dropped to 2%. The patient was regularly monitored every 3 mo without evidence of recurrence. CONCLUSION Biomarker status should be reassessed after NACT especially in luminal subtypes.

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Section: Discussionsupporting

confidence: 90%

HER2 changes to positive after neoadjuvant chemotherapy in breast cancer: A case report and literature review

Luo¹,

Jiang²,

He³

et al. 2022

WJCC

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“…Based on the performance improvements obtained by including the FISH results as an extra dimension in the reference diagnosis, it is hypothesized that the addition of more features to the ML algorithm training, that include the construction of a training supervision matrix based on unsupervised learning will improve predictive ability. This complementary information may include the basic histological subtype of the tumor, the expression pattern in lymph node metastases, the response to treatment, and patient survival, to better study the predictive variables of each tumor (2,(55)(56)(57). With this increase in the dimension of the known output variables, the performance of the test may potentially improve, specifically in the recall values, which were low in this study (IHC: 0.43 and IHC + FISH: 0.55), this may assist in determining the proportion of actual positives that were correctly identified (58).…”

Section: Discussionmentioning

confidence: 99%

HER2 classification in breast cancer cells: A new explainable machine learning application for immunohistochemistry

et al. 2022

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The immunohistochemical (IHC) evaluation of epidermal growth factor 2 (HER2) for the diagnosis of breast cancer is still qualitative with a high degree of inter-observer variability, and thus requires the incorporation of complementary techniques such as fluorescent in situ hybridization (FISH) to resolve the diagnosis. Implementing automatic algorithms to classify IHC biomarkers is crucial for typifying the tumor and deciding on therapy for each patient with better performance. The present study aims to demonstrate that, using an explainable Machine Learning (ML) model for the classification of HER2 photomicrographs, it is possible to determine criteria to improve the value of IHC analysis. We trained a logistic regression-based supervised ML model with 393 IHC microscopy images from 131 patients, to discriminate between upregulated and normal expression of the HER2 protein. Pathologists' diagnoses (IHC only) vs. the final diagnosis complemented with FISH (IHC + FISH) were used as training outputs. Basic performance metrics and receiver operating characteristic curve analysis were used together with an explainability algorithm based on Shapley Additive exPlanations (SHAP) values to understand training differences. The model could discriminate amplified IHC from normal expression with better performance when the training output was the IHC + FISH final diagnosis (IHC vs. IHC + FISH: area under the curve, 0.94 vs. 0.81). This may be explained by the increased analytical impact of the membrane distribution criteria over the global intensity of the signal, according to SHAP value interpretation. The classification model improved its performance when the training input was the final diagnosis, downplaying the weighting of the intensity of the IHC signal, suggesting that to improve pathological diagnosis before FISH consultation, it is necessary to emphasize subcellular patterns of staining.

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“…These markers are critical in identifying the appropriate therapy for breast cancer patients. [ 57–59 ] The estrogen receptor α expression is correlated with about 70% of the diagnosed breast cancer cases. [ 60 ] Estrogen receptor α is the best predictive marker for using adjuvant cancer therapies like tamoxifen and aromatase inhibitors.…”

Section: Mirs In Breast Cancermentioning

confidence: 99%

MicroRNAs and gene regulation in breast cancer

Abdalla

Singh

Bhat

2020

J Biochem & Molecular Tox

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MicroRNAs (miRs) are non–protein‐coding small RNAs that control the gene expression posttranscriptionally. miRs can regulate different cellular functions as well as many pathological conditions. Dysregulated miR expression profiles have been identified in different cancer types including lung cancer, breast cancer, and prostate cancer. The expression of miRs varies according to their roles in each specific cancer type. Although many miRs and their target genes have been identified in different cancers, data are still scant to validate those target genes and the mechanistic role of these miRs. The possibility of targeting cancer‐associated miRs is suggested to open a new field for cancer therapy. Therapeutic strategies targeting miRs involve neutralization of the oncogenic miRs by antagomirs using locked nucleic acid oligonucleotides, miR sponges, or the restoration/overexpression of tumor‐suppressing miRs that are downregulated or depleted in cancers. Although it is suggested that therapeutic applications of miRs in different pathological conditions will make a huge revolution in gene therapy, there is still an enormous challenge in employing these strategies efficiently in cancer inhibition, seemingly, due to the complexity of cancer and the current inefficient development of delivery systems for the therapeutic miRs.

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Phenotypic alterations in breast cancer associated with neoadjuvant chemotherapy: A comparison with baseline rates of change

Cited by 6 publications

References 56 publications

HER2 changes to positive after neoadjuvant chemotherapy in breast cancer: A case report and literature review

HER2 changes to positive after neoadjuvant chemotherapy in breast cancer: A case report and literature review

HER2 classification in breast cancer cells: A new explainable machine learning application for immunohistochemistry

MicroRNAs and gene regulation in breast cancer

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