Pathologic evaluation of response to neoadjuvant therapy drives treatment changes and improves long-term outcomes for breast cancer patients

Neoadjuvant therapy increases the risk of metabolic disorders and osteosarcopenia in patients with early breast cancer

BACKGROUND

The purpose of this study is to evaluate the effects of neoadjuvant therapy on glucose and lipid metabolism, bone mineral density (BMD) and muscle, and to explore the relationship between metabolic disorders and changes in body composition, so as to provide better health management strategies for breast cancer survivors.

METHODS

The clinical data of 43 patients with breast cancer who received neoadjuvant therapy in Xuanwu Hospital from January 2020 to June 2021 were analyzed retrospectively. The biochemical results, including albumin, blood glucose, triglyceride and cholesterol, were collected before neoadjuvant therapy and before surgery. The pectoral muscle area, pectoral muscle density and cancellous bone mineral density of the 12th thoracic vertebra were also measured by chest CT.

RESULTS

After neoadjuvant therapy, fasting blood glucose, triglyceride and cholesterol were significantly increased, albumin was decreased. At the same time, pectoral muscle area, pectoral muscle density and T12 BMD were decreased. After treatment, BMD was positively correlated with pectoral muscle area, R2 = 0.319, P = 0.037, and BMD was also positively correlated with pectoral muscle density, R2 = 0.329, P = 0.031. Multivariate analysis showed that BMD and pectoral muscle density were correlated with menstrual status, and pectoral muscle area was correlated with body mass index before treatment, none of which was related to glucose and lipid metabolism.

CONCLUSION

Neoadjuvant therapy can cause glucose and lipid metabolism disorder, BMD decrease and muscle reduction. BMD was positively correlated with muscle area and density after treatment, suggesting that patients had an increased chance of developing osteosarcopenia.

Pathologic tumor response to neoadjuvant therapy in resected pancreatic cancer: does it affect prognosis?

Neoadjuvant therapy (NAT) + surgical resection for pancreatic cancer (PC) has gained consensus in recent years. Pathological response (PR) is generally assessed according to the College of American Pathologists grading system, ranging from 0 (complete response) to 3 (no response). The aim of our study is to evaluate the PR in a series of resections for PC after NAT and its prognostic implication. 112 patients undergone NAT and resection for PC between 2011 and 2020 were retrospectively evaluated. PR was 0/1, 2 and 3 in 18 (15%), 79 (61%) and 29 (24%) cases, respectively. Chemotherapy regimens different from FOLFIRINOX and gemcitabine + nab-paclitaxel (OR 11.61 (2.53-53.36), p = 0.002) and lymphovascular invasion (OR 11.28 (1.89-67.23), p = 0.008) were associated to PR-3. Median follow-up was 25.8 (3.6-130.5) months. For PR-0/1, PR-2 and PR-3, median DFS was 45.8, 11.5, 4.6 months (p < 0.0001), respectively, while median OS was not reached, 27.1 and 17.5 months (p = 0.0006), respectively. At univariate analysis, PR-0/1 was significantly associated to better DFS and OS (HR 0.33 (0.17-0.67), p = 0.002; HR 0.20 (0.07-0.54), p = 0.002, respectively). At multivariate analysis, pancreaticoduodenectomy (HR 0.50 (0.30-0.84), p = 0.009), LNR (HR 27.14 (1.21-608.9), p = 0.038) and lymphovascular invasion (HR 1.99 (1.06-3.76), p = 0.033) were independently associated to DFS; pre-treatment CA 19.9 value (HR 1.00 (1.00-1.00), p = 0.025), post-treatment resectability status (HR 0.51 (0.28-0.95), p = 0.035), pancreaticoduodenectomy (HR 0.56 (0.32-0.99), p = 0.050), severe morbidity (2.99 (1.22-7.55), p = 0.017), LNR (HR 56.8 (2.08-1548.3), p = 0.017), lymphovascular invasion (HR 2.18 (1.08-4.37), p = 0.029) were independently associated to OS. PR did not reach statistical significance at multivariate analysis. A favorable PR is observed only in a limited number of cases. The prognostic role of PR, despite being promising, remains unclear and further multicentric studies are needed.

Machine Learning-Based Prediction of Distant Recurrence in Invasive Breast Carcinoma Using Clinicopathological Data: A Cross-Institutional Study

Breast cancer is the most common type of cancer worldwide. Alarmingly, approximately 30% of breast cancer cases result in disease recurrence at distant organs after treatment. Distant recurrence is more common in some subtypes such as invasive breast carcinoma (IBC). While clinicians have utilized several clinicopathological measurements to predict distant recurrences in IBC, no studies have predicted distant recurrences by combining clinicopathological evaluations of IBC tumors pre- and post-therapy with machine learning (ML) models. The goal of our study was to determine whether classification-based ML techniques could predict distant recurrences in IBC patients using key clinicopathological measurements, including pathological staging of the tumor and surrounding lymph nodes assessed both pre- and post-neoadjuvant therapy, response to therapy via standard-of-care imaging, and binary status of adjuvant therapy administered to patients. We trained and tested four clinicopathological ML models using a dataset (144 and 17 patients for training and testing, respectively) from Duke University and validated the best-performing model using an external dataset (8 patients) from Dartmouth Hitchcock Medical Center. The random forest model performed better than the C-support vector classifier, multilayer perceptron, and logistic regression models, yielding AUC values of 1.0 in the testing set and 0.75 in the validation set (p < 0.002) across both institutions, thereby demonstrating the cross-institutional portability and validity of ML models in the field of clinical research in cancer. The top-ranking clinicopathological measurement impacting the prediction of distant recurrences in IBC were identified to be tumor response to neoadjuvant therapy as evaluated via SOC imaging and pathology, which included tumor as well as node staging.

Risk of Recurrence in Patients with HER2+ Early-Stage Breast Cancer: Literature Analysis of Patient and Disease Characteristics

Human epidermal growth factor receptor 2 (HER2) overexpression occurs in 15% to 20% of patients with early-stage breast cancers (EBCs). Without HER2-targeted therapy, 30% to 50% of patients relapse within 10 years, many developing incurable metastatic disease. This literature review was designed to identify and validate patient- and disease-related factors associated with recurrence in patients with HER2+ EBC. Peer-reviewed primary research articles and congress abstracts were identified by searching MEDLINE. Articles published in English from 2019 to 2022 were included to identify contemporary treatment options. Results were analyzed for the relationship between risk factors and surrogates of HER2+ EBC recurrence to determine how identified risk factors affected HER2+ EBC recurrence. Sixty-one articles and 65 abstracts that assessed age at diagnosis, body mass index (BMI), tumor size at diagnosis, hormone receptor (HR) status, pathologic complete response (pCR) status, and biomarkers were analyzed. We confirmed the results of previously published reviews reporting residual cancer burden >0, non-pCR, and fewer tumor-infiltrating lymphocytes (TILs) as risk factors of recurrence. HR status remained an important risk factor for recurrence, with HER2+/HR+ disease more likely to recur. Two or more positive lymph nodes, higher BMI, larger primary tumor size, and low Ki67 were more commonly associated with HER2+ EBC recurrence. The identification of patient and disease factors frequently associated with HER2+ EBC recurrence in the literature provides insight into potential recurrence risk factors. Further investigation into the risk factors identified in this review could lead to improved treatments for patients at high risk for HER2+ EBC recurrence.

Nomogram for Early Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Combining Both Clinicopathological and Imaging Indicators

To construct a nomogram for early prediction of pathological complete response (pCR) in patients with breast cancer (BC) after neoadjuvant chemotherapy (NAC). A total of 257 patients with BC from the fourth hospital of Hebei Medical University were included in the study. The patients were divided into training (n = 128) and validation groups (n = 129). Variables were screened using univariate and multivariate logistic regression analyses, and the nomogram model was set up based on the training group. The training and validation groups were validated using the receiver operating characteristic (ROC) curves and calibration plots. The diagnostic value of the nomogram was evaluated using decision curve analysis (DCA). Indicators such as hormone receptor status, clinical TNM stage, and change rate in apparent diffusion coefficient of breast magnetic resonance imaging after two NAC cycles were used for nomogram construction. The calibration plots showed high consistency between nomogram-predicted and actual pCR probabilities in the training and validation groups. The areas under the curve of the ROC curve with discrimination ability were 0.942 and 0.921 in the training and validation groups, respectively. This showed an excellent discrimination ability of our nomogram for pCR prediction. Further, DCA showed favorable diagnostic value in our model. The nomogram may be instructive to clinicians for early prediction of pCR and helpful to adjust the treatment program on time in neoadjuvant management.

Neoadjuvant Therapy in Breast Cancer: Histologic Changes and Clinical Implications

Cytotoxic or endocrine therapy before surgery (neoadjuvant) for breast cancer has become standard of care, affording the opportunity to assess and quantify response in the subsequent resection specimen. Correlation with radiology, cassette mapping, and histologic review with a semi-quantitative reporting system such as residual cancer burden (RCB) provides important prognostic data that may guide further therapy. The tumor bed should be identified histologically, often as a collagenized zone devoid of normal breast epithelium, with increased vasculature. Identification of residual treated carcinoma may require careful high power examination, as residual tumor cells may be small and dyscohesive; features are widely variable and include hyperchromatic small, large, or multiple nuclei with clear, foamy, or eosinophilic cytoplasm. Calculation of RCB requires residual carcinoma span in 2 dimensions, estimated carcinoma cellularity (% area), number of involved lymph nodes, and span of largest nodal carcinoma. These RCB parameters may differ from AJCC staging measurements, which depend on only contiguous carcinoma in breast and lymph nodes.

Role and evaluation of pathologic response in early breast cancer specimens after neoadjuvant therapy: consensus statement

Pathologic evaluation of early breast cancer after neoadjuvant therapy is essential to provide prognostic information based on tumor response to treatment (pathologic complete response [pCR] or non-pCR) and to inform therapy decisions after surgery. To harmonize the pathologist’s handling of surgical specimens after neoadjuvant therapy, a panel of experts in breast cancer convened to developed a consensus on six main topics: (1) definition of pCR, (2) required clinical information, (3) gross examination and sampling, (4) microscopic examination, (5) evaluation of lymph node status, and (6) staging of residual breast tumor. The resulting consensus statements reported in this document highlight the role of an accurate evaluation of tumor response and define the minimum requirements to standardize the assessment of breast cancer specimens after neoadjuvant therapy.

Clinico-pathologic predictors of patterns of residual disease following neoadjuvant chemotherapy for breast cancer

Among breast cancer patients treated with neoadjuvant chemotherapy (NAC) who do not experience a pathologic complete response (pCR), the pattern of residual disease in the breast varies. Pre-treatment clinico-pathologic features that predict the pattern of residual tumor are not well established. To investigate this issue, we performed a detailed review of histologic sections of the post-treatment surgical specimens for 665 patients with stage I-III breast cancer treated with NAC followed by surgery from 2004 to 2014 and for whom slides of the post-NAC surgical specimen were available for review. This included 242 (36.4%) patients with hormone receptor (HR)+/HER2- cancers, 216 (32.5%) with HER2+ tumors, and 207 (31.1%) with triple negative breast cancer (TNBC). Slide review was blinded to pre-treatment clinico-pathologic features. pCR was achieved in 7.9%, 37.0%, and 37.7%, of HR+/HER2- cancers, HER2+ cancers, and TNBC respectively (p < 0.001). Among 389 patients with residual invasive cancer in whom the pattern of residual disease could be assessed, 287 (73.8%) had a scattered pattern and 102 (26.2%) had a circumscribed pattern. In both univariate and multivariate analyses, there was a significant association between tumor subtype and pattern of response. Among patients with HR+/HER2- tumors, 89.4% had a scattered pattern and only 10.6% had a circumscribed pattern. In contrast, among those with TNBC 52.8% had a circumscribed pattern and 47.2% had a scattered pattern (p < 0.001). In addition to subtype, histologic grade and tumor size at presentation were also significantly related to the pattern of residual disease in multivariate analysis, with lower grade and larger size each associated with a scattered response pattern (p = 0.002 and p = 0.01, respectively). A better understanding of the relationship between pre-treatment clinico-pathologic features of the tumor and pattern of residual disease may be of value for helping to guide post-chemotherapy surgical management.