Pathologic complete response predicts recurrence-free survival more effectively by cancer subset: results from the I-SPY 1 TRIAL--CALGB 150007/150012, ACRIN 6657

In-Breast Tumor Progression During Neoadjuvant Chemotherapy: Impact on and Factors Influencing Distant Recurrence-Free Survival

BACKGROUND

Most patients with breast cancer treated with neoadjuvant chemotherapy (NAC) experience clinical benefit, however, a small proportion progress. We aimed to characterize factors predicting in-breast tumor progression and impact on distant recurrence.

PATIENTS AND METHODS

We reviewed all patients with clinical stage I-III breast cancer treated with NAC in 2006-2021 at our institution. We compared in-breast progressive disease (PD), defined as ≥ 20% increase in tumor size, with stable disease (SD) or response. Distant recurrence-free survival (DRFS) was analyzed using the Kaplan-Meier method and Cox proportional hazards regression.

RESULTS

Of 1403 patients, 70 (5%) experienced in-breast PD, 243 (17%) SD, 560 (40%) partial response (PR), and 530 (38%) breast pathologic complete response (breast pCR, ypT0/Tis). The rate of PD varied by tumor subtype (8% in HR+/HER2-, 5% TNBC, 2% HER2+, p < 0.001). With median 48 months follow-up, the rates of DRFS were significantly different according to clinical breast response as follows: PD 56%, SD 68%, PR 82%, or breast pCR 93%, p < 0.001. In patients with PD on multivariable analysis, post-NAC grade (adjusted HR 2.9, p = 0.002) and ypT3-4 category (adjusted HR 2.4, p = 0.03) were the strongest predictors of DRFS. Combining these factors, 23% had neither, 44% had one, and 33% had both, which stratified outcome in PD with 3-year DRFS of 100%, 77%, and 30%, respectively (p < 0.001).

CONCLUSIONS

While in-breast PD during NAC is uncommon (5%), it predicts poor survival. Among patients with in-breast PD, post-NAC tumor grade and T category predict outcomes and may be useful to guide treatment escalation.

Prognostic Value of Pathological Response for Patients with Unresectable Hepatocellular Carcinoma Undergoing Conversion Surgery

Introduction

Transarterial chemoembolization combined with lenvatinib and PD-1 inhibitor (triple therapy) has displayed encouraging clinical outcomes for unresectable hepatocellular carcinoma (uHCC). We aimed to explore the prognostic value of pathological response (PR) in patients with initially uHCC who underwent conversion surgery following triple therapy and identify predictors of major pathological response (MPR).

Methods

A total of 76 patients with initially uHCC who underwent conversion surgery following triple therapy were retrospectively analyzed. PR was calculated as the proportion of nonviable tumor cell surface area of the whole tumor bed surface area. MPR was identified when PR was ≥90%. Pathological complete response (pCR) was defined as the absence of viable tumor cells.

Results

MPR and pCR were identified in 53 (69.7%) and 25 (32.9%) patients, respectively. The 1- and 2-year overall survival in patients with MPR were significantly higher than in those without MPR (100.0% and 91.3% vs. 67.7% and 19.4%; p < 0.001). The corresponding recurrence-free survival was also improved in patients with MPR compared to those without (75.9% and 50.8% vs. 22.3% and 11.2%; p < 0.001). Similar results were observed among patients with pCR and those without. Patients who achieved MPR without pCR exhibited survival rates comparable to those of patients who achieved pCR. Baseline neutrophil-to-lymphocyte ratio ≥2.6 (p = 0.016) and preoperative alpha-fetoprotein level ≥400 ng/mL (p = 0.015) were independent predictors of MPR.

Conclusion

The presence of MPR or pCR could improve prognosis in patients with initially uHCC who underwent conversion surgery following triple therapy. The PR may become a surrogate marker for predicting the prognosis of these patients.

Predictive analysis of breast cancer response to neoadjuvant chemotherapy through plasma metabolomics

PURPOSE

Preoperative chemotherapy is a critical component of breast cancer management, yet its effectiveness is not uniform. Moreover, the adverse effects associated with chemotherapy necessitate the identification of a patient subgroup that would derive the maximum benefit from this treatment. This study aimed to establish a method for predicting the response to neoadjuvant chemotherapy in breast cancer patients utilizing a metabolomic approach.

METHODS

Plasma samples were obtained from 87 breast cancer patients undergoing neoadjuvant chemotherapy at our facility, collected both before the commencement of the treatment and before the second treatment cycle. Metabolite analysis was conducted using capillary electrophoresis-mass spectrometry (CE-MS) and liquid chromatography-mass spectrometry (LC-MS). We performed comparative profiling of metabolite concentrations by assessing the metabolite profiles of patients who achieved a pathological complete response (pCR) against those who did not, both in initial and subsequent treatment cycles.

RESULTS

Significant variances were observed in the metabolite profiles between pCR and non-pCR cases, both at the onset of preoperative chemotherapy and before the second cycle. Noteworthy distinctions were also evident between the metabolite profiles from the initial and the second neoadjuvant chemotherapy courses. Furthermore, metabolite profiles exhibited variations associated with intrinsic subtypes at all assessed time points.

CONCLUSION

The application of plasma metabolomics, utilizing CE-MS and LC-MS, may serve as a tool for predicting the efficacy of neoadjuvant chemotherapy in breast cancer in the future after all necessary validations have been completed.

Early evaluation of circulating tumor DNA as marker of therapeutic efficacy and prognosis in breast cancer patients during primary systemic therapy

BACKGROUND

We assessed the potential role of serial circulating tumor DNA (ctDNA) as a biomarker to monitor treatment response to primary systemic therapy (PST) in breast cancer and evaluated the predictive value of ctDNA to further identify patients with residual disease.

METHODS

We prospectively enrolled 208 plasma samples collected at three time points (before PST, after 2 cycles of treatment, before surgery) of 72 patients with stage Ⅱ-III breast cancer. Somatic mutations in plasma samples were identified using a customized 128-gene capture panel with next-generation sequencing. The correlation between early change in ctDNA levels and treatment response or long-term clinical outcomes was assessed.

RESULTS

37 of 72 (51.4%) patients harbored detectable ctDNA alterations at baseline. Patients with complete response showed a larger decrease in ctDNA levels during PST. The median relative change of variant allele fraction (VAF) was -97.4%, -46.7%, and +21.1% for patients who subsequently had a complete response (n = 11), partial response (n = 11), and no response (n = 15) (p = 0.0012), respectively. In addition, the relative change of VAF between the pretreatment and first on-treatment blood draw exhibited the optimal predictive value to tumor response after PST (area under the curve, AUC = 0.7448, p = 0.02). More importantly, early change of ctDNA levels during treatment have significant prognostic value for patients with BC, there was a significant correlation between early decrease of VAF and longer recurrence-free survival compared to those with an VAF increase (HR = 12.54; 95% CI, 2.084 to 75.42, p = 0.0063).

CONCLUSION

Early changes of ctDNA are strongly correlated with therapeutic efficacy to PST and clinical outcomes in BC patients. The integration of preoperative ctDNA evaluation could help improving the perioperative management for BC patients receiving PST.

A prospective study of HER3 expression pre and post neoadjuvant therapy of different breast cancer subtypes: implications for HER3 imaging therapy guidance

PURPOSE

HER3, a member of the EGFR receptor family, plays a central role in driving oncogenic cell proliferation in breast cancer. Novel HER3 therapeutics are showing promising results while recently developed HER3 PET imaging modalities aid in predicting and assessing early treatment response. However, baseline HER3 expression, as well as changes in expression while on neoadjuvant therapy, have not been well-characterized. We conducted a prospective clinical study, pre- and post-neoadjuvant/systemic therapy, in patients with newly diagnosed breast cancer to determine HER3 expression, and to identify possible resistance mechanisms maintained through the HER3 receptor.

EXPERIMENTAL DESIGN

The study was conducted between May 25, 2018 and October 12, 2019. Thirty-four patients with newly diagnosed breast cancer of any subtype (ER ± , PR ± , HER2 ±) were enrolled in the study. Two core biopsy specimens were obtained from each patient at the time of diagnosis. Four patients underwent a second research biopsy following initiation of neoadjuvant/systemic therapy or systemic therapy which we define as neoadjuvant therapy. Molecular characterization of HER3 and downstream signaling nodes of the PI3K/AKT and MAPK pathways pre- and post-initiation of therapy was performed. Transcriptional validation of finings was performed in an external dataset (GSE122630).

RESULTS

Variable baseline HER3 expression was found in newly diagnosed breast cancer and correlated positively with pAKT across subtypes (r = 0.45). In patients receiving neoadjuvant/systemic therapy, changes in HER3 expression were variable. In a hormone receptor-positive (ER +/PR +/HER2-) patient, there was a statistically significant increase in HER3 expression post neoadjuvant therapy, while there was no significant change in HER3 expression in a ER +/PR +/HER2+ patient. However, both of these patients showed increased downstream signaling in the PI3K/AKT pathway. One subject with ER +/PR -/HER2- breast cancer and another subject with ER +/PR +/HER2 + breast cancer showed decreased HER3 expression. Transcriptomic findings, revealed an immune suppressive environment in patients with decreased HER3 expression post therapy.

CONCLUSION

This study demonstrates variable HER3 expression across breast cancer subtypes. HER3 expression can be assessed early, post-neoadjuvant therapy, providing valuable insight into cancer biology and potentially serving as a prognostic biomarker. Clinical translation of neoadjuvant therapy assessment can be achieved using HER3 PET imaging, offering real-time information on tumor biology and guiding personalized treatment for breast cancer patients.

Dostarlimab: Review on success story and clinical trials

The PD-1/PD-L1 pathway plays a significant role in inhibiting, escaping from immune response, and promoting self-tolerance of the tumour. Dostarlimab is a selective humanized monoclonal antibody designed to target PD-1 and block its activity with PD-L1, which further prevents the escape of tumour cells from immune surveillance. It got accelerated approval from the FDA for treating adults with mismatch repair deficient, recurrent, or advanced endometrial cancer, and studies confirmed its beneficial effects. A recently published clinical trial reported 100 % remission of advanced rectal cancer without significant side effects in the participants. This clinical trial is still going on and enrolling patients with different types of cancer, including ovarian cancer, melanoma, head and neck cancer, and breast cancer therapy. The clinical trial result gave hope and proof to the medical fraternity and patients for better treatment. The focus of this review is to summarise pre-clinical and clinical studies of Dostarlimab.

Prognostic significance of HER2 loss after HER2-targeted neoadjuvant treatment in patients with HER2-positive locally advanced breast cancer

Loss of human epidermal growth factor receptor 2 (HER2) expression can be seen in almost 25-30 % patients after HER2 receptor directed neoadjuvant treatment. These patients have unclear clinical outcomes in previous studies. We aimed to investigate the importance of HER2 loss, additionally with predictive factors for the loss of HER2. This was a retrospective and multicenter study that included 272 HER2-positive BC patients with no pathological complete response who received neoadjuvant chemotherapy plus HER2-targeted treatments. The factors that may affect the loss of HER2 detected by immunohistochemistry(IHC) and the association with survival were analyzed.The rate of HER2 loss after neoadjuvant treatments(NAT) was 27.9 % (n = 76). Disease recurrence was observed in 18(23.7 %) patients with HER2 loss, while it was detected in 62 (31.7 %) patients without HER2 loss(p = 0.23). Pre and post-NAT ER status, and post-NAT ki-67 status had a significant impact on disease-free survival(DFS) (p = 0.0012, p = 0.004, and p = 0.04, respectively).There were no significant association between DFS and loss of HER2 (p = 0.64) and dual anti-HER2 blockade (p = 0.21). Pre-NAT clinical stage (HR:1.65 p = 0.013), post-NAT LN status (HR:3.18, p = 0.02) and pre-NAT ER status (HR:0.24, p = 0.041) were significant independent prognostic factors for DFS while post-NAT residual disease in axillar tissue was an independent prognostic factor for OS (HR:1.54 p = 0.019). Moreover, age (<40 years vs ≥40 years) (p = 0.031) and tumor grade (p = 0.004) were predictive factors for HER2 loss. Our results showed that HER2 loss did not affect survivals. However, young age and being high grade tumor may predict HER2 loss.

Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer

INTRODUCTION

Histological images contain phenotypic information predictive of patient outcomes. Due to the heavy workload of pathologists, the time-consuming nature of quantitatively assessing histological features, and human eye limitations to recognize spatial patterns, manually extracting prognostic information in routine pathological workflows remains challenging. Digital pathology has facilitated the mining and quantification of these features utilizing whole-slide image (WSI) scanners and artificial intelligence (AI) algorithms. AI algorithms to identify image-based biomarkers from the tumor microenvironment (TME) have the potential to revolutionize the field of oncology, reducing delays between diagnosis and prognosis determination, allowing for rapid stratification of patients and prescription of optimal treatment regimes, thereby improving patient outcomes.

AREAS COVERED

In this review, the authors discuss how AI algorithms and digital pathology can predict breast cancer patient prognosis and treatment outcomes using image-based biomarkers, along with the challenges of adopting this technology in clinical settings.

EXPERT OPINION

The integration of AI and digital pathology presents significant potential for analyzing the TME and its diagnostic, prognostic, and predictive value in breast cancer patients. Widespread clinical adoption of AI faces ethical, regulatory, and technical challenges, although prospective trials may offer reassurance and promote uptake, ultimately improving patient outcomes by reducing diagnosis-to-prognosis delivery delays.

Explainable 3D CNN based on baseline breast DCE-MRI to give an early prediction of pathological complete response to neoadjuvant chemotherapy

BACKGROUND

So far, baseline Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has played a key role for the application of sophisticated artificial intelligence-based models using Convolutional Neural Networks (CNNs) to extract quantitative imaging information as earlier indicators of pathological Complete Response (pCR) achievement in breast cancer patients treated with neoadjuvant chemotherapy (NAC). However, these models did not exploit the DCE-MRI exams in their full geometry as 3D volume but analysed only few individual slices independently, thus neglecting the depth information.

METHOD

This study aimed to develop an explainable 3D CNN, which fulfilled the task of pCR prediction before the beginning of NAC, by leveraging the 3D information of post-contrast baseline breast DCE-MRI exams. Specifically, for each patient, the network took in input a 3D sequence containing the tumor region, which was previously automatically identified along the DCE-MRI exam. A visual explanation of the decision-making process of the network was also provided.

RESULTS

To the best of our knowledge, our proposal is competitive than other models in the field, which made use of imaging data alone, reaching a median AUC value of 81.8%, 95%CI [75.3%; 88.3%], a median accuracy value of 78.7%, 95%CI [74.8%; 82.5%], a median sensitivity value of 69.8%, 95%CI [59.6%; 79.9%] and a median specificity value of 83.3%, 95%CI [82.6%; 84.0%], respectively. The median and CIs were computed according to a 10-fold cross-validation scheme for 5 rounds.

CONCLUSION

Finally, this proposal holds high potential to support clinicians on non-invasively early pursuing or changing patient-centric NAC pathways.

Physicians' Hierarchy of Tumor Biomarkers for Optimizing Chemotherapy in Breast Cancer Care

BACKGROUND

Tumor biomarkers are regularly used to guide breast cancer treatment and clinical trial enrollment. However, there remains a lack of knowledge regarding physicians' perspectives towards biomarkers and their role in treatment optimization, where treatment intensity is reduced to minimize toxicity.

METHODS

Thirty-nine academic and community oncologists participated in semi-structured qualitative interviews, providing perspectives on optimization approaches to chemotherapy treatment. Interviews were audio-recorded, transcribed, and analyzed by 2 independent coders utilizing a constant comparative method in NVivo. Major themes and exemplary quotes were extracted. A framework outlining physicians' conception of biomarkers, and their comfortability with their use in treatment optimization, was developed.

RESULTS

In the hierarchal model of biomarkers, level 1 is comprised of standard-of-care (SoC) biomarkers, defined by a strong level of evidence, alignment with national guidelines, and widespread utilization. Level 2 includes SoC biomarkers used in alternative contexts, in which physicians expressed confidence, yet less certainty, due to a lack of data in certain subgroups. Level 3, or experimental, biomarkers created the most diverse concerns related to quality and quantity of evidence, with several additional modulators.

CONCLUSION

This study demonstrates that physicians conceptualize the use of biomarkers for treatment optimization in successive levels. This hierarchy can be used to guide trialists in the development of novel biomarkers and design of future trials.

Construction of a risk stratification model integrating ctDNA to predict response and survival in neoadjuvant-treated breast cancer

BACKGROUND

The pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) of breast cancer is closely related to a better prognosis. However, there are no reliable indicators to accurately identify which patients will achieve pCR before surgery, and a model for predicting pCR to NAC is required.

METHODS

A total of 269 breast cancer patients in Shandong Cancer Hospital and Liaocheng People's Hospital receiving anthracycline and taxane-based NAC were prospectively enrolled. Expression profiling using a 457 cancer-related gene sequencing panel (DNA sequencing) covering genes recurrently mutated in breast cancer was carried out on 243 formalin-fixed paraffin-embedded tumor biopsies samples before NAC from 243 patients. The unique personalized panel of nine individual somatic mutation genes from the constructed model was used to detect and analyze ctDNA on 216 blood samples. Blood samples were collected at indicated time points including before chemotherapy initiation, after the 1st NAC and before the 2nd NAC cycle, during intermediate evaluation, and prior to surgery. In this study, we characterized the value of gene profile mutation and circulating tumor DNA (ctDNA) in combination with clinical characteristics in the prediction of pCR before surgery and investigated the prognostic prediction. The median follow-up time for survival analysis was 898 days.

RESULTS

Firstly, we constructed a predictive NAC response model including five single nucleotide variant (SNV) mutations (TP53, SETBP1, PIK3CA, NOTCH4 and MSH2) and four copy number variation (CNV) mutations (FOXP1-gain, EGFR-gain, IL7R-gain, and NFKB1A-gain) in the breast tumor, combined with three clinical factors (luminal A, Her2 and Ki67 status). The tumor prediction model showed good discrimination of chemotherapy sensitivity for pCR and non-pCR with an AUC of 0.871 (95% CI, 0.797-0.927) in the training set, 0.771 (95% CI, 0.649-0.883) in the test set, and 0.726 (95% CI, 0.556-0.865) in an extra test set. This tumor prediction model can also effectively predict the prognosis of disease-free survival (DFS) with an AUC of 0.749 at 1 year and 0.830 at 3 years. We further screened the genes from the tumor prediction model to establish a unique personalized panel consisting of 9 individual somatic mutation genes to detect and analyze ctDNA. It was found that ctDNA positivity decreased with the passage of time during NAC, and ctDNA status can predict NAC response and metastasis recurrence. Finally, we constructed the chemotherapy prediction model combined with the tumor prediction model and pretreatment ctDNA levels, which has a better prediction effect of pCR with the AUC value of 0.961.

CONCLUSIONS

In this study, we established a chemotherapy predictive model with a non-invasive tool that is built based on genomic features, ctDNA status, as well as clinical characteristics for predicting pCR to recognize the responders and non-responders to NAC, and also predicting prognosis for DFS in breast cancer. Adding pretreatment ctDNA levels to a model containing gene profile mutation and clinical characteristics significantly improves stratification over the clinical variables alone.

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.

Value of ultrasound in assessing response to neoadjuvant chemotherapy in breast cancer

AIM

To analyse the utility of ultrasound in assessing response to neoadjuvant chemotherapy (NAC) and predicting residual cancer burden (RCB) index and pathological complete response (pCR) MATERIALS AND METHODS: This was a retrospective study with 417 patients over 7 years. The difference in longest diameter (LD) of the index lesion from baseline to end, baseline to mid, and mid to end was evaluated with respect to RCB class using logistic regression and ordered logistic regression.

RESULTS

Change in LD measurements from baseline to end, baseline to mid, and mid to end of chemotherapy as a predictor of RCB class show a negative relationship with a statistically significant association. This would suggest that a smaller change in LD measurements would be associated with an eventual higher RCB class. Change in LD measurements from baseline to end and baseline to mid chemotherapy as a predictor of pCR class show a negative relationship with a statistically significant association (p<0.05). This similarly indicates an inversely proportional relationship between changes in LD measurements and RCB class 0 for baseline to end and baseline to mid.

CONCLUSION

This study has shown significance in reducing LD measurements on ultrasound as a predictor of PCR and RCB class. This adds weight to the current practice of using ultrasound at the start, mid and end of chemotherapy cycles to monitor NACT responses.

Successful Breast Conservation After Neoadjuvant Chemotherapy in Lobular Breast Cancer: The Role of Menopausal Status in Response to Treatment

BACKGROUND

While neoadjuvant chemotherapy (NAC) has been shown to increase rates of breast conservation surgery (BCS) for breast cancer, response rates in invasive lobular carcinoma (ILC) appear lower than other histologic subtypes. Some data suggest higher response rates to NAC in premenopausal versus postmenopausal patients, but this has not been studied in ILC. We evaluated the rates of successful BCS after NAC in patients with ILC stratified by menopausal status.

PATIENTS AND METHODS

We analyzed data from a single-institution cohort of 666 patients with stage I-III hormone receptor positive HER-2 negative ILC. We used t-tests, chi-squared tests, and multivariable logistic regression to investigate rates of NAC use, attempted BCS, and associations between NAC and successful BCS by menopausal status.

RESULTS

In 217 premenopausal and 449 postmenopausal patients, NAC was used more often in the premenopausal group (15.2% vs. 9.8%, respectively, p = 0.041). Among those who attempted breast conservation (51.3% of pre- and 64.8% of postmenopausal cohorts), NAC was not associated with successful BCS in either group. Interestingly, for postmenopausal patients, receipt of NAC was significantly associated with increased rates of completion mastectomy in those who had positive margins at the first attempt at BCS.

CONCLUSION

NAC was not associated with successful BCS in either premenopausal or postmenopausal patients with ILC. Although premenopausal patients were more likely to receive NAC, these data suggest that menopausal status may not be a good predictor of response to chemotherapy. Better predictors of response and more efficacious treatment for patients with ILC are needed.

Evaluation of the relationship between Ki67 expression level and neoadjuvant treatment response and prognosis in breast cancer based on the Neo-Bioscore staging system

BACKGROUND

Neoadjuvant chemotherapy (NAC) is widely used in the treatment of primary breast cancer. Different staging systems have been developed to evaluate the residual tumor after NAC and classify patients into different prognostic groups. Ki67, a proliferation marker, has been shown to be useful in predicting treatment response and prognosis. We aimed to investigate the prognostic importance Neo-Bioscore stage and pretreatment and posttreatment Ki67 levels in breast cancer patients who received NAC and correlations between Neo-Bioscore stage and pretreatment and posttreatment Ki67 levels.

METHODS

A total of 176 invasive breast carcinoma patients who underwent NAC were included in the study. Ki67 levels were evaluated by immunohistochemical methods in Trucut biopsy and surgical excision specimens. Patients were classified into prognostic groups using the Neo-Bioscore staging system.

RESULTS

Patients with high pretreatment Ki67 score were more likely to be in the higher Neo-Bioscore risk group (p < 0.001). Patients with a high posttreatment Ki67 score were more likely to be in the higher Neo-Bioscore prognostic risk group (p < 0.001). Overall survival (OS) and disease-free survival (DFS) were shorter in patients with high posttreatment Ki67 scores and in patients in the higher Neo-Bioscore risk group. We also determined a cutoff 37% for pathological complete response.

CONCLUSION

Neo-Bioscore staging system is found to be important in predicting survival. The posttreatment Ki67 level is more important than pretreatment Ki67 level in predicting survival.

Analysis of neoadjuvant chemotherapy for breast cancer: a 20-year retrospective analysis of patients of a single institution

BACKGROUND

Neoadjuvant chemotherapy (NAC) has been widely applied in operable breast cancer patients. This study aim to identify the predictive factors of overall survival(OS) and recurrence free survival (RFS) in breast cancer patients who received NAC from a single Chinese institution.

PATIENTS AND METHODS

There were 646 patients recruited in this study. All the patients were treated at department of Surgical Oncology, Sir Run Run Shaw Hospital between February 25, 1999 and August 22, 2018. The relevant clinicopathological and follow-up data were collected retrospectively. RFS and OS were assessed using the Kaplan-Meier method. Multivariate Cox proportional hazards model was also employed. Multi-variate logistic regression model was simulated to predict pathologic complete response (pCR).

RESULTS

In total, 118 patients (18.2%) achieved pCR during NAC. The 5-year OS was 94.6% versus 78.1% in patients with and without pCR, respectively (P < 0.001). The 5-year RFS was 95.3% and 72.7%, respectively (P < 0.001). No difference was detected among molecular subtypes of 5-year RFS in patients obtained pCR. Factors independently predicting RFS were HER2-positive subtype (hazard ratio(HR), 1.906; P = 0.004), triple-negative breast cancer (TNBC) (HR,2.079; P = 0.003), lymph node positive after NAC(HR,2.939; P < 0.001), pCR (HR, 0.396;P = 0.010), and clinical stage III (HR,2.950; P = 0.016). Multi-variate logistic regression model was simulated to predict the pCR rate after NAC, according to clinical stage, molecular subtype, ki-67, LVSI, treatment period and histology. In the ROC curve analysis, the AUC of the nomogram was 0.734 (95%CI,0.867-12.867).

CONCLUSIONS

Following NAC, we found that pCR positively correlated with prognosis and the molecular subtype was a prognostic factor.

Imaging Challenges in Diagnosing Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) refers to a heterogeneous group of carcinomas that have more aggressive biologic features, faster growth, and a propensity for early distant metastasis and recurrence compared with other breast cancer subtypes. Due to the aggressiveness and rapid growth of TNBCs, there are specific imaging challenges associated with their timely and accurate diagnosis. TNBCs commonly manifest initially as circumscribed masses and therefore lack the typical features of a primary breast malignancy, such as irregular shape, spiculated margins, and desmoplastic reaction. Given the potential for misinterpretation, review of the multimodality imaging appearances of TNBCs is important for guiding the radiologist in distinguishing TNBCs from benign conditions. Rather than manifesting as a screening-detected cancer, TNBC typically appears clinically as a palpable area of concern that most commonly corresponds to a discrete mass at mammography, US, and MRI. The combination of circumscribed margins and hypoechoic to anechoic echogenicity may lead to TNBC being misinterpreted as a benign fibroadenoma or cyst. Therefore, careful mammographic and sonographic evaluation with US image optimization can help avoid misinterpretation. Radiologists should recognize the characteristics of TNBCs that can mimic benign entities, as well as the subtle features of TNBCs that should raise concern for malignancy and aid in timely and accurate diagnosis. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Predicting pathological complete response to neoadjuvant systemic therapy for triple-negative breast cancers using deep learning on multiparametric MRIs

We trained and validated a deep learning model that can predict the treatment response to neoadjuvant systemic therapy (NAST) for patients with triple negative breast cancer (TNBC). Dynamic contrast enhanced (DCE) MRI and diffusion-weighted imaging (DWI) of the pre-treatment (baseline) and after four cycles (C4) of doxorubicin/cyclophosphamide treatment were used as inputs to the model for prediction of pathologic complete response (pCR). Based on the standard pCR definition that includes disease status in either breast or axilla, the model achieved areas under the receiver operating characteristic curves (AUCs) of 0.96 ± 0.05, 0.78 ± 0.09, 0.88 ± 0.02, and 0.76 ± 0.03, for the training, validation, testing, and prospective testing groups, respectively. For the pCR status of breast only, the retrained model achieved prediction AUCs of 0.97 ± 0.04, 0.82 ± 0.10, 0.86 ± 0.03, and 0.83 ± 0.02, for the training, validation, testing, and prospective testing groups, respectively. Thus, the developed deep learning model is highly promising for predicting the treatment response to NAST of TNBC.Clinical Relevance- Deep learning based on serial and multiparametric MRIs can potentially distinguish TNBC patients with pCR from non-pCR at the early stage of neoadjuvant systemic therapy, potentially enabling more personalized treatment of TNBC patients.

Integration of multiomics data shows down regulation of mismatch repair and tubulin pathways in triple-negative chemotherapy-resistant breast tumors

BACKGROUND

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype. Patients with TNBC are primarily treated with neoadjuvant chemotherapy (NAC). The response to NAC is prognostic, with reductions in overall survival and disease-free survival rates in those patients who do not achieve a pathological complete response (pCR). Based on this premise, we hypothesized that paired analysis of primary and residual TNBC tumors following NAC could identify unique biomarkers associated with post-NAC recurrence.

METHODS AND RESULTS

We investigated 24 samples from 12 non-LAR TNBC patients with paired pre- and post-NAC data, including four patients with recurrence shortly after surgery (< 24 months) and eight who remained recurrence-free (> 48 months). These tumors were collected from a prospective NAC breast cancer study (BEAUTY) conducted at the Mayo Clinic. Differential expression analysis of pre-NAC biopsies showed minimal gene expression differences between early recurrent and nonrecurrent TNBC tumors; however, post-NAC samples demonstrated significant alterations in expression patterns in response to intervention. Topological-level differences associated with early recurrence were implicated in 251 gene sets, and an independent assessment of microarray gene expression data from the 9 paired non-LAR samples available in the NAC I-SPY1 trial confirmed 56 gene sets. Within these 56 gene sets, 113 genes were observed to be differentially expressed in the I-SPY1 and BEAUTY post-NAC studies. An independent (n = 392) breast cancer dataset with relapse-free survival (RFS) data was used to refine our gene list to a 17-gene signature. A threefold cross-validation analysis of the gene signature with the combined BEAUTY and I-SPY1 data yielded an average AUC of 0.88 for six machine-learning models. Due to the limited number of studies with pre- and post-NAC TNBC tumor data, further validation of the signature is needed.

CONCLUSION

Analysis of multiomics data from post-NAC TNBC chemoresistant tumors showed down regulation of mismatch repair and tubulin pathways. Additionally, we identified a 17-gene signature in TNBC associated with post-NAC recurrence enriched with down-regulated immune genes.

Radiomic tumor phenotypes augment molecular profiling in predicting recurrence free survival after breast neoadjuvant chemotherapy

BACKGROUND

Early changes in breast intratumor heterogeneity during neoadjuvant chemotherapy may reflect the tumor's ability to adapt and evade treatment. We investigated the combination of precision medicine predictors of genomic and MRI data towards improved prediction of recurrence free survival (RFS).

METHODS

A total of 100 women from the ACRIN 6657/I-SPY 1 trial were retrospectively analyzed. We estimated MammaPrint, PAM50 ROR-S, and p53 mutation scores from publicly available gene expression data and generated four, voxel-wise 3-D radiomic kinetic maps from DCE-MR images at both pre- and early-treatment time points. Within the primary lesion from each kinetic map, features of change in radiomic heterogeneity were summarized into 6 principal components.

RESULTS

We identify two imaging phenotypes of change in intratumor heterogeneity (p < 0.01) demonstrating significant Kaplan-Meier curve separation (p < 0.001). Adding phenotypes to established prognostic factors, functional tumor volume (FTV), MammaPrint, PAM50, and p53 scores in a Cox regression model improves the concordance statistic for predicting RFS from 0.73 to 0.79 (p = 0.002).

CONCLUSIONS

These results demonstrate an important step in combining personalized molecular signatures and longitudinal imaging data towards improved prognosis.

Validation of Predictive Analyses for Interim Decisions in Clinical Trials

PURPOSE

Adaptive clinical trials use algorithms to predict, during the study, patient outcomes and final study results. These predictions trigger interim decisions, such as early discontinuation of the trial, and can change the course of the study. Poor selection of the Prediction Analyses and Interim Decisions (PAID) plan in an adaptive clinical trial can have negative consequences, including the risk of exposing patients to ineffective or toxic treatments.

METHODS

We present an approach that leverages data sets from completed trials to evaluate and compare candidate PAIDs using interpretable validation metrics. The goal is to determine whether and how to incorporate predictions into major interim decisions in a clinical trial. Candidate PAIDs can differ in several aspects, such as the prediction models used, timing of interim analyses, and potential use of external data sets. To illustrate our approach, we considered a randomized clinical trial in glioblastoma. The study design includes interim futility analyses on the basis of the predictive probability that the final analysis, at the completion of the study, will provide significant evidence of treatment effects. We examined various PAIDs with different levels of complexity to investigate if the use of biomarkers, external data, or novel algorithms improved interim decisions in the glioblastoma clinical trial.

RESULTS

Validation analyses on the basis of completed trials and electronic health records support the selection of algorithms, predictive models, and other aspects of PAIDs for use in adaptive clinical trials. By contrast, PAID evaluations on the basis of arbitrarily defined ad hoc simulation scenarios, which are not tailored to previous clinical data and experience, tend to overvalue complex prediction procedures and produce poor estimates of trial operating characteristics such as power and the number of enrolled patients.

CONCLUSION

Validation analyses on the basis of completed trials and real world data support the selection of predictive models, interim analysis rules, and other aspects of PAIDs in future clinical trials.

Prediction of pathologic complete response to neoadjuvant systemic therapy in triple negative breast cancer using deep learning on multiparametric MRI

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer. Neoadjuvant systemic therapy (NAST) followed by surgery are currently standard of care for TNBC with 50-60% of patients achieving pathologic complete response (pCR). We investigated ability of deep learning (DL) on dynamic contrast enhanced (DCE) MRI and diffusion weighted imaging acquired early during NAST to predict TNBC patients' pCR status in the breast. During the development phase using the images of 130 TNBC patients, the DL model achieved areas under the receiver operating characteristic curves (AUCs) of 0.97 ± 0.04 and 0.82 ± 0.10 for the training and the validation, respectively. The model achieved an AUC of 0.86 ± 0.03 when evaluated in the independent testing group of 32 patients. In an additional prospective blinded testing group of 48 patients, the model achieved an AUC of 0.83 ± 0.02. These results demonstrated that DL based on multiparametric MRI can potentially differentiate TNBC patients with pCR or non-pCR in the breast early during NAST.

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.

Postneoadjuvant treatment for triple-negative breast cancer

PURPOSE OF REVIEW

Triple-negative breast cancer (TNBC) has been conventionally associated with poor prognosis, as a result of limited therapeutic options. In the early setting, prognosis is informed by clinical-pathological factors; for patients receiving neoadjuvant treatments, pathological complete response (pCR) is the strongest factor. In this review, we mapped the landscape of clinical trials in the postneoadjuvant space, and identified three patterns of clinical trial design.

RECENT FINDINGS

For patients at higher risk, effective postneoadjuvant treatments are of paramount importance to address a high clinical need. Postneoadjuvant risk-adapted treatments have demonstrated to improve survival in patients at high of recurrence.

SUMMARY

Patients at high risk have indication for adjuvant treatment intensification, informed by baseline clinical, pathological or molecular factors (type 1 approach), on the presence, extent and molecular characteristics of the residual disease at the time of surgery (type 2) or on risk factors assessed in the postsurgical setting (type 3), for example, circulating tumour DNA. Most of the past trials were based on type 2 approaches, for example, with capecitabine and Olaparib. Few trials were based on a type 1 approach, notably pembrolizumab for early TNBC. The clinical validity of type 3 approaches is under investigation in several ongoing trials.

Metabolomics by NMR Combined with Machine Learning to Predict Neoadjuvant Chemotherapy Response for Breast Cancer

Simple Summary

Neoadjuvant chemotherapy (NACT) is offered to breast cancer (BC) patients to downstage the disease. However, some patients may not respond to NACT, being resistant. We used the serum metabolic profile by Nuclear Magnetic Resonance (NMR) combined with disease characteristics to differentiate between sensitive and resistant BC patients. We obtained accuracy above 80% for the response prediction and showcased how NMR can substantially enhance the prediction of response to NACT.

Abstract

Neoadjuvant chemotherapy (NACT) is offered to patients with operable or inoperable breast cancer (BC) to downstage the disease. Clinical responses to NACT may vary depending on a few known clinical and biological features, but the diversity of responses to NACT is not fully understood. In this study, 80 women had their metabolite profiles of pre-treatment sera analyzed for potential NACT response biomarker candidates in combination with immunohistochemical parameters using Nuclear Magnetic Resonance (NMR). Sixty-four percent of the patients were resistant to chemotherapy. NMR, hormonal receptors (HR), human epidermal growth factor receptor 2 (HER2), and the nuclear protein Ki67 were combined through machine learning (ML) to predict the response to NACT. Metabolites such as leucine, formate, valine, and proline, along with hormone receptor status, were discriminants of response to NACT. The glyoxylate and dicarboxylate metabolism was found to be involved in the resistance to NACT. We obtained an accuracy in excess of 80% for the prediction of response to NACT combining metabolomic and tumor profile data. Our results suggest that NMR data can substantially enhance the prediction of response to NACT when used in combination with already known response prediction factors.

The effectiveness and safety of neoadjuvant pertuzumab and trastuzumab in women with locally advanced, inflammatory, or early-stage human epidermal growth factor receptor 2-positive breast cancer: Turkish Oncology Group study

In our study, we aimed to evaluate the pathological response rates and side effect profile of adding pertuzumab to the treatment of HER2+ locally advanced, inflammatory, or early-stage breast cancer. This study was conducted by the Turkish Oncology Group (TOG) with data collected from 32 centers. Our study was multicentric, and a total of 364 patients were included. The median age of the patients was 49 years (18-85 years). Two hundred fifteen (60%) of the cases were hormone receptor/HER2+ positive(ER+ or PR+, or both), and 149 (40%) of them were HER2-rich (ER and PR negative). The number of complete responses was 124 (54%) in the docetaxel+trastuzumab+pertuzumab arm and 102 (45%) in the paclitaxel+trastuzumab+pertuzumab arm, and there was no difference between the groups in terms of complete response. In 226 (62%) patients with complete response, a significant correlation was found with DCIS, tumor focality, removed lymph node, and ER status P < 0.05. Anemia, nausea, vomiting, myalgia, alopecia, and mucosal inflammation were significantly higher in the docetaxel arm, P < 0.05. In our study, no statistical difference was found between the before-after echocardiography values. DCIS positivity in biopsy before neoadjuvant chemotherapy, tumor focality; the number of lymph nodes removed and ER status were found to be associated with pCR. In conclusion, we think that studies evaluating pCR-related clinicopathological variables and radiological imaging features will play a critical role in the development of nonsurgical treatment approaches.

Clinical trials of immunotherapy in triple-negative breast cancer

PURPOSE

Immunotherapy has started to transform the treatment of triple-negative breast cancer (TNBC), in part due to the unique immunogenicity of this breast cancer subtype. This review summarizes clinical studies of immunotherapy in advanced and early-stage TNBC.

FINDINGS

Initial studies of checkpoint blockade monotherapy demonstrated occasional responses, especially in patients with untreated programmed death-ligand 1 (PD-L1) positive advanced TNBC, but failed to confirm a survival advantage over chemotherapy. Nonetheless, pembrolizumab monotherapy has tumor agnostic approval for microsatellite instability-high or high tumor mutational burden cancers, and thus can be considered for select patients with advanced TNBC. Combination chemoimmunotherapy approaches have been more successful, and pembrolizumab is approved for PD-L1 positive advanced TNBC in combination with chemotherapy. This success has been translated to the curative setting, where pembrolizumab is now approved in combination with neoadjuvant chemotherapy for high-risk early-stage TNBC.

CONCLUSION

Immunotherapy has been a welcome addition to the growing armamentarium for TNBC, but responses remain limited to a subset of patients. Innovative strategies are under investigation in an attempt to induce immune responses in resistant tumors-with regimens incorporating small-molecule inhibitors, novel immune checkpoint targets, and intratumoral injections that directly alter the tumor microenvironment. As the focus shifts toward the use of immunotherapy for early-stage TNBC, it will be critical to identify those who derive the most benefit from treatment, given the potential for irreversible autoimmune toxicity and the lack of predictive accuracy of PD-L1 expression in the early-stage setting.

Trophoblast Cell Surface Antigen 2 gene (TACSTD2) expression in primary breast cancer

PURPOSE

Trophoblast Cell Surface Antigen 2 (TROP2) is a glycoprotein expressed in many cancers. A TROP2 antibody-drug conjugate (ADC) was effective in metastatic triple-negative breast cancer (TNBC). We studied TROP2 gene (TACSTD2) expression and associations with tumor and clinical characteristics, as well as selected external genes in primary breast cancer.

METHODS

TACSTD2 gene expression was evaluated using microarray data from I-SPY 1 (n = 149), METABRIC (n = 1992), and TCGA (n = 817). Associations with clinical features (Kruskal-Wallis test, all datasets), chemotherapy response (Wilcoxon rank sum test, I-SPY 1), recurrence free survival (Cox proportional hazard model, I-SPY 1 and METABRIC), and selected genes (Pearson correlations, all datasets) were determined.

RESULTS

TACSTD2 gene expression was detectable in all breast cancer subtypes, with a wide range of expression (all datasets). TACSTD2 gene expression was lower in HER2 + than HR + /HER2- and TNBC (METABRIC: p = 0.03, TCGA p = 0.007), and in HER2 + enriched and luminal B breast cancer (METABRIC: p < 0.001, TCGA: p < 0.001). TACSTD2 expression was higher in grade I vs. II/III tumors (METABRIC: p < 0.001). No association with chemotherapy response (I-SPY 1) or recurrence free survival (I-SPY 1 and METABRIC) was seen. TACSTD2 has significant positive correlations with the expression of epithelial/adhesion genes and proliferative genes, but was inversely correlated with immune genes.

CONCLUSION

TACSTD2 gene expression was seen in all breast cancer subtypes particularly luminal A and TNBC, and correlated with the expression of genes involved in cell epithelial transformation, adhesion, and proliferation, which contribute to tumor growth. These results support the investigation of TROP2 ADC in all subtypes of breast cancer.

Advances in the Prevention and Treatment of Obesity-Driven Effects in Breast Cancers

Obesity and associated chronic inflammation were shown to facilitate breast cancer (BC) growth and metastasis. Leptin, adiponectin, estrogen, and several pro-inflammatory cytokines are involved in the development of obesity-driven BC through the activation of multiple oncogenic and pro-inflammatory pathways. The aim of this study was to assess the reported mechanisms of obesity-induced breast carcinogenesis and effectiveness of conventional and complementary BC therapies. We screened published original articles, reviews, and meta-analyses that addressed the involvement of obesity-related signaling mechanisms in BC development, BC treatment/prevention approaches, and posttreatment complications. PubMed, Medline, eMedicine, National Library of Medicine (NLM), and ReleMed databases were used to retrieve relevant studies using a set of keywords, including "obesity," "oncogenic signaling pathways," "inflammation," "surgery," "radiotherapy," "conventional therapies," and "diet." Multiple studies indicated that effective BC treatment requires the involvement of diet- and exercise-based approaches in obese postmenopausal women. Furthermore, active lifestyle and diet-related interventions improved the patients' overall quality of life and minimized adverse side effects after traditional BC treatment, including postsurgical lymphedema, post-chemo nausea, vomiting, and fatigue. Further investigation of beneficial effects of diet and physical activity may help improve obesity-linked cancer therapies.

Robustness Evaluation of a Deep Learning Model on Sagittal and Axial Breast DCE-MRIs to Predict Pathological Complete Response to Neoadjuvant Chemotherapy

To date, some artificial intelligence (AI) methods have exploited Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to identify finer tumor properties as potential earlier indicators of pathological Complete Response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). However, they work either for sagittal or axial MRI protocols. More flexible AI tools, to be used easily in clinical practice across various institutions in accordance with its own imaging acquisition protocol, are required. Here, we addressed this topic by developing an AI method based on deep learning in giving an early prediction of pCR at various DCE-MRI protocols (axial and sagittal). Sagittal DCE-MRIs refer to 151 patients (42 pCR; 109 non-pCR) from the public I-SPY1 TRIAL database (DB); axial DCE-MRIs are related to 74 patients (22 pCR; 52 non-pCR) from a private DB provided by Istituto Tumori “Giovanni Paolo II” in Bari (Italy). By merging the features extracted from baseline MRIs with some pre-treatment clinical variables, accuracies of 84.4% and 77.3% and AUC values of 80.3% and 78.0% were achieved on the independent tests related to the public DB and the private DB, respectively. Overall, the presented method has shown to be robust regardless of the specific MRI protocol.

MRI in the Setting of Neoadjuvant Treatment of Breast Cancer

Neoadjuvant therapy may reduce tumor burden preoperatively, allowing breast conservation treatment for tumors previously unresectable or requiring mastectomy without reducing disease-free survival. Oncologists can also use the response of the tumor to neoadjuvant chemotherapy (NAC) to identify treatment likely to be successful against any unknown potential distant metastasis. Accurate preoperative estimations of tumor size are necessary to guide appropriate treatment with minimal delays and can provide prognostic information. Clinical breast examination and mammography are inaccurate methods for measuring tumor size after NAC and can over- and underestimate residual disease. While US is commonly used to measure changes in tumor size during NAC due to its availability and low cost, MRI remains more accurate and simultaneously images the entire breast and axilla. No method is sufficiently accurate at predicting complete pathological response that would obviate the need for surgery. Diffusion-weighted MRI, MR spectroscopy, and MRI-based radiomics are emerging fields that potentially increase the predictive accuracy of tumor response to NAC.

Screening of organoids derived from patients with breast cancer implicates the repressor NCOR2 in cytotoxic stress response and antitumor immunity

Resistance to antitumor treatment contributes to patient mortality. Functional proteomic screening of organoids derived from chemotherapy-treated patients with breast cancer identified nuclear receptor corepressor 2 (NCOR2) histone deacetylase as an inhibitor of cytotoxic stress response and antitumor immunity. High NCOR2 in the tumors of patients with breast cancer predicted chemotherapy refractoriness, tumor recurrence and poor prognosis. Molecular studies revealed that NCOR2 inhibits antitumor treatment by regulating histone deacetylase 3 (HDAC3) to repress interferon regulatory factor 1 (IRF-1)-dependent gene expression and interferon (IFN) signaling. Reducing NCOR2 or impeding its epigenetic activity by modifying its interaction with HDAC3 enhanced chemotherapy responsiveness and restored antitumor immunity. An adeno-associated viral NCOR2-HDAC3 competitor potentiated chemotherapy and immune checkpoint therapy in culture and in vivo by permitting transcription of IRF-1-regulated proapoptosis and inflammatory genes to increase IFN-γ signaling. The findings illustrate the utility of patient-derived organoids for drug discovery and suggest that targeting stress and inflammatory-repressor complexes such as NCOR2-HDAC3 could overcome treatment resistance and improve the outcome of patients with cancer.

Exosomal Metabolic Signatures Are Associated with Differential Response to Neoadjuvant Chemotherapy in Patients with Breast Cancer

Neoadjuvant chemotherapy (NAC) is commonly used in breast cancer (BC) patients to increase eligibility for breast-conserving surgery. Only 30% of patients with BC show pathologic complete response (pCR) after NAC, and residual disease (RD) is associated with poor long-term prognosis. A critical barrier to improving NAC outcomes in patients with BC is the limited understanding of the mechanisms underlying differential treatment outcomes. In this study, we evaluated the ability of exosomal metabolic profiles to predict NAC response in patients with BC. Exosomes isolated from the plasma of patients after NAC were used for metabolomic analyses to identify exosomal metabolic signatures associated with the NAC response. Among the 16 BC patients who received NAC, eight had a pCR, and eight had RD. Patients with RD had 2.52-fold higher exosome concentration in their plasma than those with pCR and showed significant enrichment of various metabolic pathways, including citrate cycle, urea cycle, porphyrin metabolism, glycolysis, and gluconeogenesis. Additionally, the relative exosomal levels of succinate and lactate were significantly higher in patients with RD than in those with pCR. These data suggest that plasma exosomal metabolic signatures could be associated with differential NAC outcomes in BC patients and provide insight into the metabolic determinants of NAC response in patients with BC.

Combined Perioperative Lapatinib and Trastuzumab in Early HER2-Positive Breast Cancer Identifies Early Responders: Randomized UK EPHOS-B Trial Long-Term Results

PURPOSE

EPHOS-B aimed to determine whether perioperative anti-HER2 therapy inhibited proliferation and/or increased apoptosis in HER2-positive breast cancer.

PATIENTS AND METHODS

This randomized phase II, two-part, multicenter trial included newly diagnosed women with HER2-positive invasive breast cancer due to undergo surgery. Patients were randomized to: part 1 (1:2:2), no treatment (control), trastuzumab or lapatinib; part 2 (1:1:2) control, trastuzumab, or lapatinib and trastuzumab combination. Treatment was given for 11 days presurgery. Coprimary endpoints were change in Ki67 and apoptosis between baseline and surgery tumor samples (biologic response: ≥30% change). Central pathology review scored residual cancer burden (RCB). Relapse-free survival (RFS) explored long-term effects.

RESULTS

Between November 2010 and September 2015, 257 patients were randomized (part 1: control 22, trastuzumab 57, lapatinib 51; part 2: control 29, trastuzumab 32, combination 66). Ki67 response was evaluable for 223 patients: in part 1 Ki67 response occurred in 29/44 (66%) lapatinib versus 18/49 (37%) trastuzumab (P = 0.007) and 1/22 (5%) control (P < 0.0001); in part 2 in 36/49 (74%) combination versus 14/31 (45%) trastuzumab (P = 0.02) and 2/28 (7%) control (P < 0.0001). No significant increase in apoptosis after 11 days was seen in treatment groups. Six patients achieved complete pathologic response (pCR, RCB0) and 13 RCB1, all but two in the combination group. After 6 years median follow-up, 28 (11%) had recurrence and 19 (7%) died. No recurrences or deaths were observed among patients who achieved a pCR. Ki67% falls ≥50% associated with fewer recurrences (P = 0.002).

CONCLUSIONS

Early response after short duration anti-HER2 dual therapy identifies cancers dependent on the HER2 pathway providing a strategy for exploring risk-adapted individualized treatment de-escalation.

Current Treatment and Future Trends of Immunotherapy in Breast Cancer

Immunotherapy continues to redefine the solid tumor treatment landscape with inhibitors of the PD-L1/PD-1 immune checkpoint having the most widespread impact. As the most common cancer diagnosed worldwide, there is significant interest in the development of immunotherapy for the treatment of breast cancer in both the early and metastatic settings. Recently reported results of several clinical trials have identified potential roles for immunotherapy agents alone or in combination with standard treatment for early and metastatic disease. While trials to date have been promising, immunotherapy thus far has been shown to benefit only a select group of patients with breast cancer, defined by tumor subtype, PD-L1 expression, and line of therapy. With over 250 trials ongoing, emerging data will enable the further refinement of breast cancer immunotherapy strategies. The integration of multiple putative biomarkers and consideration of dynamic markers of early response or resistance may inform optimal patient selection for immunotherapy investigation and integration into clinical practice. This review will summarize the current evidence for immune-checkpoint blockade (ICB) in the treatment of early and metastatic breast cancer and highlight current and potential future biomarkers of therapeutic response.

Development and validation of a nomogram based on pretreatment dynamic contrast-enhanced MRI for the prediction of pathologic response after neoadjuvant chemotherapy for triple-negative breast cancer

OBJECTIVES

To develop a nomogram based on pretreatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in patients with triple-negative breast cancer (TNBC).

METHODS

A total of 108 female patients with TNBC treated with neoadjuvant chemotherapy followed by surgery between January 2017 and October 2020 were enrolled. The patients were randomly divided into the primary cohort (n = 87) and validation cohort (n = 21) at a ratio of 4:1. The pretreatment DCE-MRI and clinicopathological features were reviewed and recorded. Univariate analysis and multivariate logistic regression analyses were used to determine the independent predictors of pCR in the primary cohort. A nomogram was developed based on the predictors, and the predictive performance of the nomogram was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). The validation cohort was used to test the predictive model.

RESULTS

Tumor volume measured on DCE-MRI, time to peak (TTP), and androgen receptor (AR) status were identified as independent predictors of pCR. The AUCs of the nomogram were 0.84 (95% CI: 0.75-0.93) and 0.79 (95% CI: 0.59-0.99) in the primary cohort and validation cohort, respectively.

CONCLUSIONS

Pretreatment DCE-MRI could predict pCR after NAC in patients with TNBC. The nomogram can be used to predict the probability of pCR and may help individualize treatment.

KEY POINTS

• Pretreatment DCE-MRI findings can predict pathologic complete response (pCR) after neoadjuvant chemotherapy in patients with triple-negative breast cancer. • A nomogram based on the independent predictors of tumor volume measured on DCE-MRI, time to peak, and androgen receptor status could help personalized cancer treatment in TNBC patients.

Development of Gene Expression-Based Random Forest Model for Predicting Neoadjuvant Chemotherapy Response in Triple-Negative Breast Cancer

Simple Summary

Only 20–50% of patients with triple negative breast cancer achieve a pathological complete response from neoadjuvant chemotherapy, a strong indicator of patient survival. Therefore, there is an urgent need for a reliable predictive model of the patient’s pathological complete response prior to actual treatment. The purpose of this study was to develop such a model based on random forest recursive feature elimination and to benchmark the performance of the proposed model against existing predictive models. Our study suggests that an 86-gene-based random forest model associated to DNA repair and cell cycle mechanisms can provide reliable predictions of neoadjuvant chemotherapy response in patients with triple negative breast cancer.

Abstract

Neoadjuvant chemotherapy (NAC) response is an important indicator of patient survival in triple negative breast cancer (TNBC), but predicting chemosensitivity remains a challenge in clinical practice. We developed an 86-gene-based random forest (RF) classifier capable of predicting neoadjuvant chemotherapy response (pathological Complete Response (pCR) or Residual Disease (RD)) in TNBC patients. The performance of pCR classification of the proposed model was evaluated by Receiver Operating Characteristic (ROC) curve and Precision Recall (PR) curve. The AUROC and AUPRC of the proposed model on the test set were 0.891 and 0.829, respectively. At a predefined specificity (>90%), the proposed model shows a superior sensitivity compared to the best performing reported NAC response prediction model (69.2% vs. 36.9%). Moreover, the predicted pCR status by the model well explains the distance recurrence free survival (DRFS) of TNBC patients. In addition, the pCR probabilities of the proposed model using the expression profiles of the CCLE TNBC cell lines show a high Spearman rank correlation with cyclophosphamide sensitivity in the TNBC cell lines (SRCC =0.697, p-value =0.031). Associations between the 86 genes and DNA repair/cell cycle mechanisms were provided through function enrichment analysis. Our study suggests that the random forest-based prediction model provides a reliable prediction of the clinical response to neoadjuvant chemotherapy and may explain chemosensitivity in TNBC.

Neoadjuvant Chemotherapy and Immunotherapy in Luminal B-like Breast Cancer: Results of the Phase II GIADA Trial

PURPOSE

The role of immunotherapy in hormone receptor (HR)-positive, HER2-negative breast cancer is underexplored.

PATIENTS AND METHODS

The neoadjuvant phase II GIADA trial (NCT04659551, EUDRACT 2016-004665-10) enrolled stage II-IIIA premenopausal patients with Luminal B (LumB)-like breast cancer (HR-positive/HER2-negative, Ki67 ≥ 20%, and/or histologic grade 3). Patients received: three 21-day cycles of epirubicin/cyclophosphamide followed by eight 14-day cycles of nivolumab, triptorelin started concomitantly to chemotherapy, and exemestane started concomitantly to nivolumab. Primary endpoint was pathologic complete response (pCR; ypT0/is, ypN0).

RESULTS

A pCR was achieved by 7/43 patients [16.3%; 95% confidence interval (CI), 7.4-34.9]; the rate of residual cancer burden class 0-I was 25.6%. pCR rate was significantly higher for patients with PAM50 Basal breast cancer (4/8, 50%) as compared with other subtypes (LumA 9.1%; LumB 8.3%; P = 0.017). Tumor-infiltrating lymphocytes (TIL), immune-related gene-expression signatures, and specific immune cell subpopulations by multiplex immunofluorescence were significantly associated with pCR. A combined score of Basal subtype and TILs had an AUC of 0.95 (95% CI, 0.89-1.00) for pCR prediction. According to multiplex immunofluorescence, a switch to a more immune-activated tumor microenvironment occurred following exposure to anthracyclines. Most common grade ≥3 treatment-related adverse events (AE) during nivolumab were γ-glutamyltransferase (16.7%), alanine aminotransferase (16.7%), and aspartate aminotransferase (9.5%) increase. Most common immune-related AEs were endocrinopathies (all grades 1-2; including adrenal insufficiency, n = 1).

CONCLUSIONS

Luminal B-like breast cancers with a Basal molecular subtype and/or a state of immune activation may respond to sequential anthracyclines and anti-PD-1. Our data generate hypotheses that, if validated, could guide immunotherapy development in this context.

Advancing precision medicine for acute respiratory distress syndrome

Acute respiratory distress syndrome (ARDS) is a heterogeneous clinical syndrome. Understanding of the complex pathways involved in lung injury pathogenesis, resolution, and repair has grown considerably in recent decades. Nevertheless, to date, only therapies targeting ventilation-induced lung injury have consistently proven beneficial, and despite these gains, ARDS morbidity and mortality remain high. Many candidate therapies with promise in preclinical studies have been ineffective in human trials, probably at least in part due to clinical and biological heterogeneity that modifies treatment responsiveness in human ARDS. A precision medicine approach to ARDS seeks to better account for this heterogeneity by matching therapies to subgroups of patients that are anticipated to be most likely to benefit, which initially might be identified in part by assessing for heterogeneity of treatment effect in clinical trials. In October 2019, the US National Heart, Lung, and Blood Institute convened a workshop of multidisciplinary experts to explore research opportunities and challenges for accelerating precision medicine in ARDS. Topics of discussion included the rationale and challenges for a precision medicine approach in ARDS, the roles of preclinical ARDS models in precision medicine, essential features of cohort studies to advance precision medicine, and novel approaches to clinical trials to support development and validation of a precision medicine strategy. In this Position Paper, we summarise workshop discussions, recommendations, and unresolved questions for advancing precision medicine in ARDS. Although the workshop took place before the COVID-19 pandemic began, the pandemic has highlighted the urgent need for precision therapies for ARDS as the global scientific community grapples with many of the key concepts, innovations, and challenges discussed at this workshop.

Individualized model for predicting pathological complete response to neoadjuvant chemotherapy in patients with breast cancer: A multicenter study

BACKGROUND

Pathological complete response (pCR) is considered a surrogate for favorable survival in breast cancer (BC) patients treated with neoadjuvant chemotherapy (NACT), which is the goal of NACT. This study aimed to develop and validate a nomogram for predicting the pCR probability of BC patients after NACT based on the clinicopathological features.

METHODS

A retrospective analysis of 527 BC patients treated with NACT between January 2018 and December 2021 from two institutions was conducted. Univariate and multivariate logistic regression analyses were performed to select the most useful predictors from the training cohort (n = 225), and then a nomogram model was developed. The performance of the nomogram was evaluated with respect to its discrimination, calibration, and clinical usefulness. Internal validation and external validation were performed in an independent validation cohort of 96 and 205 consecutive BC patients, respectively.

RESULTS

Among the 18 clinicopathological features, five variables were selected to develop the prediction model, including age, American Joint Committee on Cancer (AJCC) T stage, Ki67 index before NACT, human epidermal growth factor receptor 2 (HER2), and hormone receptor (HR) status. The model showed good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.825 (95% CI, 0.772 to 0.878) in the training cohort, and 0.755 (95% CI, 0.658 to 0.851) and 0.79 (95% CI, 0.724 to 0.856) in the internal and external validation cohorts, respectively. The calibration curve presented good agreement between prediction by nomogram and actual observation, and decision curve analysis (DCA) indicated that the nomogram had good net benefits in clinical scenarios.

CONCLUSION

This study constructed a validated nomogram based on age, AJCC T stage, Ki67 index before NACT, HER2, and HR status, which could be non-invasively applied to personalize the prediction of pCR in BC patients treated with NACT.

Pathologic evaluation of specimens after neoadjuvant chemotherapy in breast cancer: Current recommendations and challenges

Neoadjuvant chemotherapy is increasingly used to optimize breast conservation surgery and is becoming a standard of care in a subset of breast cancer patients. An accurate pathologic assessment is crucial in guiding clinical decisions and subsequent management and prognosis. This review aims to summarize the most current literature, recommendations, and challenges in the pathologic evaluation of breast cancer after neoadjuvant chemotherapy. Included are the most current definitions of the different types of tumor response, the underlying factors that can affect tumor response, how to assess lymph nodes, margins, and tumor markers post-neoadjuvant chemotherapy, as well as the different classification systems a pathologist can use to assess residual disease. In this era of de-escalation of surgical treatment, studies on imaging techniques to assess residual disease and avoid surgery after neoadjuvant chemotherapy have also been done. However, at least for now, surgical treatment remains the preferred practice. As such, pathologists play an increasingly critical role in standardizing assessment of residual disease post-neoadjuvant chemotherapy, and in optimizing the knowledge gained by this approach to breast cancer therapy.

Evaluation of PD-L1 and tumor infiltrating lymphocytes in paired pretreatment biopsies and post neoadjuvant chemotherapy surgical specimens of breast carcinoma

Herein it was evaluated the impact of PD-L1 immunohistochemical expression and stromal tumor-infiltrating lymphocyte (sTIL) counts in pretreatment needle core biopsy on response to neoadjuvant chemotherapy (NACT) for patients with breast carcinomas (BC). In 127 paired pre- and post-NACT BC specimens, immunohistochemical expression of PD-L1 was evaluated in stroma and in neoplastic cells. In the same samples sTILs were semi-quantified in tumor stroma. Post-NACT specimens were histologically rated as having residual cancer burden (RCB of any degree), or with complete pathological response (pCR). PD-L1 expression and higher sTIL counts were associated with histological grade 3 BC. PD-L1 expression was also associated with the non-luminal-HER2+ and triple negative immunohistochemical profiles of BC. Pathological complete response was associated with histological grade 3 tumors, and with the non-luminal-HER2+ and triple negative profiles. Additionally, our results support an association between PD-L1 expression and pCR to NACT. It was also observed that there is a trend to reduction of sTIL counts in the post-NACT specimens of patients with pCR. Of note, PD-L1 was expressed in half of the hormone receptor positive cases, a finding that might expand the potential use of immune checkpoint inhibitors for BC patients.

An evaluation of the healthcare costs of metastatic breast cancer: A retrospective matched cohort study

To determine the economic burden of metastatic breast cancer (MBC) in Taiwan, we conducted a national retrospective claim database analysis to evaluate the incremental healthcare costs and utilization of MBC patients as compared to their breast cancer (BC) and breast cancer free (BCF) counterparts.Data were obtained from the National Health Insurance Claim Database and the Taiwan Cancer Registry database between 2012 and 2015. All healthcare utilization and costs were calculated on a per-patient-per-month (PPPM) basis and were compared among groups using the generalized linear model adjusting for age group, residential area, and Charlson comorbidity index group.A total of 1,606 MBC patients were matched to 6,424 BC patients and 6,424 BCF patients. The majority of overall MBC healthcare costs were attributed to outpatient costs (75.1%), followed by inpatient (23.2%) and emergency room costs (1.7%). The PPPM total healthcare costs of the MBC, BC, and BCF groups were TWD 7,422, 14,425, and 2,114, respectively. The adjusted PPPM total healthcare cost ratio of MBC to BCF was 4.1. Compared to BCF patients, the patients receiving both human epidermal growth factor receptor 2-targeted therapy and endocrine therapy incurred 28.1 times PPPM total costs. The adjusted PPPM total healthcare cost ratio of recurrent MBC to BCF was 2.3, while the ratio was 12.2 in the de novo MBC group.Patients with MBC are associated with substantial economic burden, particularly in outpatient costs. The study findings could be useful for MBC-related economic evaluations and health resource allocation.

Comparing Biomarkers for Predicting Pathological Responses to Neoadjuvant Therapy in HER2-Positive Breast Cancer: A Systematic Review and Meta-Analysis

INTRODUCTION

The predictive strength and accuracy of some biomarkers for the pathological complete response (pCR) to neoadjuvant therapy for HER2-positive breast cancer remain unclear. This study aimed to compare the accuracy of the HER2-enriched subtype and the presence of PIK3CA mutations, namely, TILs, HRs, and Ki-67, in predicting the pCR to HER2-positive breast cancer therapy.

METHODS

We screened studies that included pCR predicted by one of the following biomarkers: the HER2-enriched subtype and the presence of PIK3CA mutations, TILs, HRs, or Ki-67. We then calculated the pooled sensitivity, specificity, positive and negative predictive values (PPVs and NPVs, respectively), and positive and negative likelihood ratios (LRs). Summary receiver operating characteristic (SROC) curves and areas under the curve (AUCs) were used to estimate the diagnostic accuracy.

RESULTS

The pooled estimates of sensitivity and specificity for the HER2-enriched subtype and the presence of PIK3CA mutations, namely, TILs, HRs, and Ki-67, were 0.66 and 0.62, 0.85 and 0.27, 0.49 and 0.61, 0.54 and 0.64, and 0.68 and 0.51, respectively. The AUC of the HER2-enriched subtype was significantly higher (0.71) than those for the presence of TILs (0.59, p = 0.003), HRs (0.65, p = 0.003), and Ki-67 (0.62, p = 0.005). The AUC of the HER2-enriched subtype had a tendency to be higher than that of the presence of PIK3CA mutations (0.58, p = 0.220). Moreover, it had relatively high PPV (0.58) and LR+ (1.77), similar NPV (0.73), and low LR- (0.54) compared with the other four biomarkers.

CONCLUSIONS

The HER2-enriched subtype has a moderate breast cancer diagnostic accuracy, which is better than those of the presence of PIK3CA mutations, TILs, HRs, and Ki-67.

Molecular Classification of Breast Cancer Utilizing Long Non-Coding RNA (lncRNA) Transcriptomes Identifies Novel Diagnostic lncRNA Panel for Triple-Negative Breast Cancer

Simple Summary

Breast cancer is the most commonly diagnosed cancer in women today and accounts for thousands of cancer-related deaths each year. While some breast cancer subtypes can be easily diagnosed and targeted for therapy, triple-negative breast cancer, which lacks receptor expression, is the most challenging to diagnose and treat. In this study, we use multiple RNA sequencing data to look specifically at long non-coding RNA (lncRNA) expression portraits at the transcript level and to identify lncRNA-based biomarkers associated with each breast cancer subtype. Receiver operating characteristic (ROC) analysis was used to validate their diagnostic potential, which was validated in two independent cohorts. Several lncRNA transcripts were found to be enriched in TNBC across all validation cohorts. Binary regression analysis identified a four lncRNA transcript signature with the highest diagnostic power for TNBC as potential novel biomarkers for diagnostic and therapeutic intervention. Interestingly, several of the identified lncRNAs were shown to have prognostic potential in TNBC.

Abstract

Breast cancer remains the world’s most prevalent cancer, responsible for around 685,000 deaths globally despite international research efforts and advances in clinical management. While estrogen receptor positive (ER+), progesterone receptor positive (PR+), and human epidermal growth factor receptor positive (HER2+) subtypes are easily classified and can be targeted, there remains no direct diagnostic test for triple-negative breast cancer (TNBC), except for the lack of receptors expression. The identification of long non-coding RNAs (lncRNAs) and the roles they play in cancer progression has recently proven to be beneficial. In the current study, we utilize RNA sequencing data to identify lncRNA-based biomarkers associated with TNBC, ER+ subtypes, and normal breast tissue. The Marker Finder algorithm identified the lncRNA transcript panel most associated with each molecular subtype and the receiver operating characteristic (ROC) analysis was used to validate the diagnostic potential (area under the curve (AUC) of ≥8.0 and p value < 0.0001). Focusing on TNBC, findings from the discovery cohort were validated in an additional two cohorts, identifying 13 common lncRNA transcripts enriched in TNBC. Binary regression analysis identified a four lncRNA transcript signature (ENST00000425820.1, ENST00000448208.5, ENST00000521666.1, and ENST00000650510.1) with the highest diagnostic power for TNBC. The ENST00000671612.1 lncRNA transcript correlated with worse refractory free survival (RFS). Our data provides a step towards finding a novel diagnostic lncRNA-based panel for TNBC with potential therapeutic implications.

Neoadjuvant docetaxel plus carboplatin vs epirubicin plus cyclophosphamide followed by docetaxel in triple-negative, early-stage breast cancer (NeoCART): Results from a multicenter, randomized controlled, open-label phase II trial

Previous studies have shown that the addition of carboplatin to neoadjuvant chemotherapy improved the pathologic complete response (pCR) rate in patients suffering from triple-negative breast cancer (TNBC) and patients who obtained a pCR could achieve prolonged event-free survival (EFS) and overall survival (OS). However, no studies have assessed the effects of the combination of docetaxel and carboplatin without anthracycline with taxane-based and anthracycline-based regimens. The NeoCART study was designed as a multicenter, randomized controlled, open-label, phase II trial to assess the efficacy and safety of docetaxel combined with carboplatin in untreated stage II-III TNBC. All eligible patients were randomly assigned, at a 1:1 ratio, to an experimental docetaxel plus carboplatin (DCb) for six cycles group (DCb group) or an epirubicin plus cyclophosphamide for four cycles followed by docetaxel for four cycles group (EC-D group). PCR (ypT0/is ypN0) was evaluated as the primary outcome. Between 1 September 2016 and 31 December 2019, 93 patients were randomly assigned and 88 patients were evaluated for the primary endpoint (44 patients in each group). In the primary endpoint analysis, 27 patients in the DCb group (61.4%, 95% CI 47.0-75.8) and 17 patients in the EC-D group achieved a pCR (38.6%, 95% CI 24.3-53.0; odds ratio 2.52, 95% CI 2.4-43.1; P_{noninferiority} = .004). Noninferiority was met, and the DCb regimen was confirmed to be superior to the EC-D regimen (P = .044, superiority margin of 5%). At the end of the 37-month median follow-up period, OS and EFS rates were equivalent in both groups.