Neoadjuvant Treatment Response Research Articles

Unveiling the relationship between the UGT2B gene family and neoadjuvant treatment response in patients with HER2-positive breast cancer.

e12651 Background: Achieving a pathological complete response (pCR) following neoadjuvant treatment (NAT) in HER2-positive tumors significantly enhances patient survival, resulting in a remarkable 92% reduction in mortality risk. However, it is crucial to identify biomarkers capable of predicting the NAT response, moving towards more personalized medicine. This approach would enable the identification of patients likely to respond to specific therapies, thereby facilitating the implementation of targeted treatment strategies. Our study aims to identify potential molecular biomarkers predictive of NAT response through a comprehensive analysis of differential gene expression in tissue samples from patients with HER2-positive, hormone receptor (HR)-negative breast cancer. Methods: Total RNA was extracted from FFPE tissue samples from two independent cohorts of women diagnosed with localized or locally advanced HER2-positive, HR-negative breast cancer. These patients underwent NAT in several hospitals in Andalusia, Spain. Standard chemotherapy, including taxanes and/or anthracyclines, and targeted anti-HER2 treatment such as trastuzumab and pertuzumab were administered. The patients were stratified into responders (R), achieving pCR, and non-responders (nR). In a discovery cohort (n=20), RNA hybridization using Clariom D microarray was conducted to discern differentially expressed transcripts between R and nR. Subsequently, gene expression patterns, and related pathways involved in NAT response, were analyzed using Transcriptome Analysis Console (TAC). The selected transcripts then underwent validation through quantitative PCR (qPCR) in an independent cohort (n=40). The area under the receiver operating characteristic (ROC) curve was used to evaluate the sensitivity and specificity of the genes as predictive biomarkers. Results: Microarray analysis of the discovery cohort, revealed 954 differentially expressed transcripts between R and nR, following criteria of fold change greater than 1.5 or less than -1.5 and a p-value below 0.05. Among these, 643 were upregulated, while 311 were downregulated in nR compared to R. Bioinformatic analysis and a comprehensive literature review guided the selection of genes UGT2B10, UGT2B11, UGT2B15, UGT2B17, and UGT2B28 for further investigation. Subsequent qPCR analysis in the validation cohort, demonstrated the overexpression of these genes in non-responders, with statistical significance achieved only for UGT2B15. Conclusions: The UGT2B family genes encode enzymes involved in glucuronidation and metabolic pathways associated with drug detoxification and elimination. Our findings suggest that variations in the UGT2B15 expression may impact the metabolism rate of specific drugs, underscoring the necessity for additional research in this area.

Assessing response and adherence to neoadjuvant treatment in soft tissue sarcomas of the extremities.

e23559 Background: The standard treatment for large, intermediate, and high-grade extremity soft tissue sarcoma (ESTS) involves surgery and radiotherapy. The addition of chemotherapy has improved oncological outcomes, such as reduced recurrence and prolonged survival. However, the relationship between neoadjuvant treatment and tumor response in ESTS is not well understood. Pathological evaluation of viable tumor cells after treatment is an indicator of tumor response, yet the proportion of viable tumor cells remains high, particularly in high-risk sarcomas. Combining radiotherapy with chemotherapy may enhance the response of tumor cells. Objective: This study aims to evaluate the effects of neoadjuvant chemotherapy (NCT) and chemoradiotherapy (CRT) on patients with high-risk ESTS and to determine its impact on the pathological response. Methods: We enrolled adult patients with high-risk ESTS (SARCULATOR OS < 60%) at a single center in Latin America. The study was designed to analyze the pathological response in patients with high-risk sarcomas who underwent neoadjuvant CRT and resection and to correlate these responses with various variables. The treatment response was evaluated using the EORTC–STBSG criteria. Results: Thirty-four patients were recruited, with a median age of 44 years (IQR 23-74). Fifty-two percent had T3 tumors (mean size 18 cm), and 79.4% were diagnosed at stage III. The predominant histological subtypes were synovial sarcoma (35.3%) and dedifferentiated liposarcoma (29.4%). The most used NCT regimen was doxorubicin/ifosfamide (84.4%), with 62.5% receiving a dosage intensity of Q4W, affected by overcapacity, and 25% received Q3W. Concurrent CRT was administered to 64.7% of patients, with a median delay to onset of 36 days due to overcapacity (43.5%). Only 79.4% completed three preoperative cycles of treatment. As for the response, 41.2% had an EORTC-E response, and 17.6% had an EORTC-D response. Grade 3 neutropenia and anemia occurred in 17.2% and 8.8% of patients, respectively. Disease progression during NCT was observed in six patients. No significant difference was found in the pathological response between the Q4W and Q3W dosage intensities (p = 0.62). Conclusions: The impact of NCT on pathological responses was not significantly high, which may be due to the predominant histological type in our series. The main cause of treatment delay at our center was due to overcapacity; however, this did not influence the pathological response relative to dose intensity. Additional follow-up assessments are required to determine the influence on relapse-free and overall survival based on the response after preoperative CRT.

Genomic analysis of the tumor microenvironment in Asian and Native Hawaiian and Pacific Islander patients with triple negative breast cancer.

e12644 Background: Triple negative breast cancer (TNBC) accounts for approximately 10- 20% of breast cancer cases and has the worst overall survival and prognosis compared to other subtypes. Unlike other subtypes, patients with TNBC are responsive to neoadjuvant therapy. A higher percent of stromal tumor infiltrating lymophocytes (sTILs) in the tumor microenvironment of TNBC correlates with an improved prognosis. Previous studies demonstrate that Native Hawaiians and Pacific Islanders (NHPIs) and Asians have equivocal percent of sTILs. Yet NHPIs have a higher mortality rate at 24.9% compared to Asians at 11%. Our study aims to molecularly characterize the tumor microenvironment in a diverse population of patients with TNBC who received neoadjuvant therapy. Methods: Retrospective chart review was conducted on 12 Asians and 12 NHPI patients with early stage TNBC from 2015 to 2022 from a medical oncology clinic within the Hawai’i Pacific Health system. Patient data collected include age at diagnosis of breast cancer, histology, BMI, clinical stage, sTILs, residual cancer burden, neoadjuvant treatment, and pathologic complete response. Core biopsy samples were processed and analyzed using the nCounter Breast Cancer 360 Panel. Further analysis was obtained using the nSolver Advanced Analysis Module. Results: Patient characteristics are listed in the table. Most patients had the basal-like immunosuppressed TNBC subtype. Among differentially expressed genes between races, 31 genes were statistically significant. NHPIs expressed more IL12RB2 and MYC, and Asians expressed more FGFR4 and MLPH. Among responders compared to non-responders, 5 genes were statistically significant. Responders expressed more STC1 and PTGER3, and non-responders expressed more CRAYAB, TMPRSS2, and CHAD. Among old and young patients, 132 genes were statistically significant. Older patients expressed more FUT3 and CKMT1, and younger patients expressed more MMP7 and MIA. Conclusions: In this pilot study, the molecular and genetic differences suggest that the tumor microenvironment may play a role in treatment outcomes between NHPIs and Asians. Further analysis will be conducted to determine the differences in immune cell infiltration. [Table: see text]

Can the Pathological Response in Patients with Locally Advanced Gastric Cancer Receiving Neoadjuvant Treatment Be Predicted by the CEA/Albumin and CRP/Albumin Ratios?

Background: The purposes of neoadjuvant chemotherapy are to tumor size to improve the tumor removal rate, extend survival, and prevent metastasis. In this study, the importance of CRP/albumin ratio and CEA/albumin ratio in the prediction of neoadjuvant treatment response in gastric cancer patients was evaluated. Methods: This study retrospectively included 135 gastric cancer patients who received neoadjuvant chemotherapy at Çukurova University Balcalı Hospital between January 2018 and December 2023. Preoperative CRP/albumin and CEA/albumin ratios were compared according to treatment response and multivariate logistic regression analysis was performed to determine the potential importance of these ratios in predicting pathological response. Results: The mean age of the 135 patients was 58.79 ± 10.83 (min = 26-max = 78). The CRP/albumin and CEA/albumin ratios were found to be significantly lower in patients who did not respond to neoadjuvant therapy. Each 1-unit increase in the CRP/albumin ratio was associated with a 1.16-fold decrease in the odds of pathological complete response to neoadjuvant therapy. Both CRP/albumin and CEA/albumin ratios were found to be significant in distinguishing neoadjuvant therapy response. The optimal cut-off value was 2.74 for the CRP/albumin ratio (sensitivity = 60%, specificity = 78.4%) and 1.40 for the CEA/albumin ratio (sensitivity = 74.2%, specificity = 67.6%). Values below these cut-off points favored neoadjuvant therapy response. Pathological complete response to neoadjuvant therapy was 4.75 times higher in patients with a CRP/albumin ratio below 2.74 and 5.14 times higher in patients with a CEA/albumin ratio below 1.40. Conclusions: Findings demonstrate that in patients with locally advanced gastric cancer receiving neoadjuvant treatment, CRP/Albumin and CEA/Albumin ratios are significant markers of pathological response.

Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment.

Clinical trials involving systemic neoadjuvant treatments in breast cancer aim to shrink tumors before surgery while simultaneously allowing for controlled evaluation of biomarkers, toxicity, and suppression of distant (occult) metastatic disease. Yet neoadjuvant clinical trials are rarely preceded by preclinical testing involving neoadjuvant treatment, surgery, and post-surgery monitoring of the disease. Here we used a mouse model of spontaneous metastasis occurring after surgical removal of orthotopically implanted primary tumors to develop a predictive mathematical model of neoadjuvant treatment response to sunitinib, a receptor tyrosine kinase inhibitor (RTKI). Treatment outcomes were used to validate a novel mathematical kinetics-pharmacodynamics model predictive of perioperative disease progression. Longitudinal measurements of presurgical primary tumor size and postsurgical metastatic burden were compiled using 128 mice receiving variable neoadjuvant treatment doses and schedules (released publicly at https://zenodo.org/records/10607753). A non-linear mixed-effects modeling approach quantified inter-animal variabilities in metastatic dynamics and survival, and machine-learning algorithms were applied to investigate the significance of several biomarkers at resection as predictors of individual kinetics. Biomarkers included circulating tumor- and immune-based cells (circulating tumor cells and myeloid-derived suppressor cells) as well as immunohistochemical tumor proteins (CD31 and Ki67). Our computational simulations show that neoadjuvant RTKI treatment inhibits primary tumor growth but has little efficacy in preventing (micro)-metastatic disease progression after surgery and treatment cessation. Machine learning algorithms that included support vector machines, random forests, and artificial neural networks, confirmed a lack of definitive biomarkers, which shows the value of preclinical modeling studies to identify potential failures that should be avoided clinically.

Abstract 7607: Single-cell genomics study before and after neoadjuvant therapy for triple-negative breast cancer

Abstract Breast cancer has become the most common malignant tumor globally. Neoadjuvant chemotherapy (NAC) has become an important treatment option for locally advanced breast cancer, capable of eliminating circulating tumor cells (micrometastases) and achieving downstaging objectives. However, even after completing neoadjuvant chemotherapy or receiving local treatment, some patients may still have residual lesions, which are a key factor in distant recurrence and metastasis. The existing clinical biomarker tests such as CEA, CA-153, and imaging methods are unable to accurately assess the condition early on. Although tissue pathology biopsy of tumor specimens is the gold standard for clinical pathological diagnosis, it also has certain limitations. Traditional high-throughput sequencing technology for breast cancer primarily identifies "driver genes" associated with the occurrence and development of breast cancer. However, it has limitations in identifying genomic structural changes and subclones of breast cancer, and it overlooks the heterogeneity among tumor cells. The emergence of single-cell RNA sequencing (scRNA-seq) technology can detect these heterogeneous individuals, decode the heterogeneity of breast cancer cells, refine molecular subtypes, and open up new pathways to overcome drug resistance. We collected samples from 6 patients with locally advanced triple-negative breast cancer (ER-, PR-, HER2-negative, defined as HER2 immunohistochemistry 0, 1+ or 2+ but FISH-negative). We compared the cell subgroups and their gene expression in biopsy specimens before neoadjuvant chemotherapy with those in post-treatment specimens obtained during surgery, analyzing changes in cell composition in the tumor microenvironment before and after immunotherapy to identify specific cell types potentially contributing to the outcome of immunotherapy, particularly cell subtypes associated with T-cell clonal proliferation. We found that in the group with good neoadjuvant treatment response (Miller-Payne grade 4-5), B cells, CD4+ T cells, CD8+ T cells, and Tregs cells were all significantly downregulated, while endothelial cells and aSMA-positive fibroblast cells were significantly upregulated. By comparing changes in the immune microenvironment between patients with and without T-cell clonal proliferation before and after treatment, we revealed the differentiation patterns of various immune cells in immunotherapy and potential mechanisms of action. Citation Format: Yingkuan Shao, Kailun Xu. Single-cell genomics study before and after neoadjuvant therapy for triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7607.

The Revolution in Breast Cancer Diagnostics: From Visual Inspection of Histopathology Slides to Using Desktop Tissue Analysers for Automated Nanomechanical Profiling of Tumours.

We aim to develop new portable desktop tissue analysers (DTAs) to provide fast, low-cost, and precise test results for fast nanomechanical profiling of tumours. This paper will explain the reasoning for choosing indentation-type atomic force microscopy (IT-AFM) to reveal the functional details of cancer. Determining the subtype, cancer stage, and prognosis will be possible, which aids in choosing the best treatment. DTAs are based on fast IT-AFM at the size of a small box that can be made for a low budget compared to other clinical imaging tools. The DTAs can work in remote areas and all parts of the world. There are a number of direct benefits: First, it is no longer needed to wait a week for the pathology report as the test will only take 10 min. Second, it avoids the complicated steps of making histopathology slides and saves costs of labour. Third, computers and robots are more consistent, more reliable, and more economical than human workers which may result in fewer diagnostic errors. Fourth, the IT-AFM analysis is capable of distinguishing between various cancer subtypes. Fifth, the IT-AFM analysis could reveal new insights about why immunotherapy fails. Sixth, IT-AFM may provide new insights into the neoadjuvant treatment response. Seventh, the healthcare system saves money by reducing diagnostic backlogs. Eighth, the results are stored on a central server and can be accessed to develop strategies to prevent cancer. To bring the IT-AFM technology from the bench to the operation theatre, a fast IT-AFM sensor needs to be developed and integrated into the DTAs.

Predicting Neoadjuvant Treatment Response in Triple-Negative Breast Cancer Using Machine Learning.

Neoadjuvant chemotherapy (NAC) is the standard treatment for early-stage triple negative breast cancer (TNBC). The primary endpoint of NAC is a pathological complete response (pCR). NAC results in pCR in only 30-40% of TNBC patients. Tumor-infiltrating lymphocytes (TILs), Ki67 and phosphohistone H3 (pH3) are a few known biomarkers to predict NAC response. Currently, systematic evaluation of the combined value of these biomarkers in predicting NAC response is lacking. In this study, the predictive value of markers derived from H&E and IHC stained biopsy tissue was comprehensively evaluated using a supervised machine learning (ML)-based approach. Identifying predictive biomarkers could help guide therapeutic decisions by enabling precise stratification of TNBC patients into responders and partial or non-responders. Serial sections from core needle biopsies (n = 76) were stained with H&E and immunohistochemically for the Ki67 and pH3 markers, followed by whole-slide image (WSI) generation. The serial section stains in H&E stain, Ki67 and pH3 markers formed WSI triplets for each patient. The resulting WSI triplets were co-registered with H&E WSIs serving as the reference. Separate mask region-based CNN (MRCNN) models were trained with annotated H&E, Ki67 and pH3 images for detecting tumor cells, stromal and intratumoral TILs (sTILs and tTILs), Ki67+, and pH3+ cells. Top image patches with a high density of cells of interest were identified as hotspots. Best classifiers for NAC response prediction were identified by training multiple ML models and evaluating their performance by accuracy, area under curve, and confusion matrix analyses. Highest prediction accuracy was achieved when hotspot regions were identified by tTIL counts and each hotspot was represented by measures of tTILs, sTILs, tumor cells, Ki67+, and pH3+ features. Regardless of the hotspot selection metric, a complementary use of multiple histological features (tTILs, sTILs) and molecular biomarkers (Ki67 and pH3) resulted in top ranked performance at the patient level. Overall, our results emphasize that prediction models for NAC response should be based on biomarkers in combination rather than in isolation. Our study provides compelling evidence to support the use of ML-based models to predict NAC response in patients with TNBC.

Predicting the Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Using Soluble Immune Checkpoints.

Introduction: Personalizing neoadjuvant therapy for locally advanced rectal cancer (LARC) requires identifying biomarkers that predict treatment response. This study aims to evaluate soluble immune checkpoints (sICPs) as predictive markers for neoadjuvant treatment response in LARC patients located in the middle and lower rectum. Materials and Methods: This prospective study included patients diagnosed with clinical stage T3 or T4 rectal cancer (RC) based on pelvic magnetic resonance imaging, with or without pelvic lymph node involvement. The modified Ryan scoring system was used to assess the response to neoadjuvant chemoradiotherapy (nCRT). Blood samples were collected from all RC patients before initiating nCRT. Various sICPs (sCD25, 4-1BB, B7.2, free active TGF-β1, CTLA-4, PD-L1, PD-1, Tim-3, LAG-3, galectin-9), along with age, gender, stage, blood cell counts, and biochemical variables, were recorded and compared based on tumor regression grade (TRG). Results: Among 38 participants, lymphocyte count was higher, and platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), and platelet count were lower in patients with complete/near-complete response (TRG 0/1). In addition, TRG 0/1 patients had significantly lower levels of soluble galectin-9 than TRG 2/3 patients. Furthermore, platelet count was the only parameter that showed a significant difference among the three groups (TRG 0/1, TRG 2, and TRG 3). PLR demonstrated the highest sensitivity and specificity, with >80% for both measures. Conclusions: Lymphocyte count, PLR, NLR, platelet count, and galectin-9 may help predict favorable neoadjuvant treatment response in LARC patients, although without providing a definitive outcome. Personalized therapy based on these markers could enhance treatment decision making in LARC management.

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

BackgroundNeoadjuvant 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.MethodsA 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.ResultsPatients 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.ConclusionNeo-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.

Correlation between Baseline Conventional Ultrasounds, Shear-Wave Elastography Indicators, and Neoadjuvant Therapy Efficacy in Triple-Negative Breast Cancer.

In patients with triple-negative breast cancer (TNBC)-the subtype with the poorest prognosis among breast cancers-it is crucial to assess the response to the currently widely employed neoadjuvant treatment (NAT) approaches. This study investigates the correlation between baseline conventional ultrasound (US) and shear-wave elastography (SWE) indicators and the pathological response of TNBC following NAT, with a specific focus on assessing predictive capability in the baseline state. This retrospective analysis was conducted by extracting baseline US features and SWE parameters, categorizing patients based on postoperative pathological grading. A univariate analysis was employed to determine the relationship between ultrasound indicators and pathological reactions. Additionally, we employed a receiver operating characteristic (ROC) curve analysis and multivariate logistic regression methods to evaluate the predictive potential of the baseline US indicators. This study comprised 106 TNBC patients, with 30 (28.30%) in a nonmajor histological response (NMHR) group and 76 (71.70%) in a major histological response (MHR) group. Following the univariate analysis, we found that T staging, dmax values, volumes, margin changes, skin alterations (i.e., thickening and invasion), retromammary space invasions, and supraclavicular lymph node abnormalities were significantly associated with pathological efficacy (p < 0.05). Combining clinical information with either US or SWE independently yielded baseline predictive abilities, with AUCs of 0.816 and 0.734, respectively. Notably, the combined model demonstrated an improved AUC of 0.827, with an accuracy of 76.41%, a sensitivity of 90.47%, a specificity of 55.81%, and statistical significance (p < 0.01). The baseline US and SWE indicators for TNBC exhibited a strong relationship with NAT response, offering predictive insights before treatment initiation, to a considerable extent.

Two-photon imaging reveals histopathological changes in the gastric tumor microenvironment induced by neoadjuvant treatment.

There is a close association between tumor response and survival in gastric cancer patients after receiving neoadjuvant treatment. An accurate and rapid assessment of therapeutic efficacy would be helpful for subsequent treatments and individual prognosis. At present, pathological examination is the gold standard for evaluating treatment response, however, it requires additional staining and the process is tedious, labor-intensive, as well as time-consuming. Here, we introduce a label-free imaging technique, two-photon imaging, to evaluate histopathological changes induced by pre-operative therapy, with a focus on assessing tumor regression as well as stromal response. Imaging data show that two-photon imaging allows label-free, rapid visualization of various aspects of pathological alterations in tumor microenvironment such as fibrotic reaction, inflammatory cell infiltration, mucinous response, isolated residual tumor cells. Moreover, a semi-automatic image processing approach is developed to extract the collagen morphological features, and statistical results show that there are significant differences in collagen area, length, width, cross-link space between the gastric cancer tissues with and without treatment. With the advent of a portable, miniaturized two-photon imaging device, we have enough reason to believe that this technique will become as an important auxiliary diagnostic tool in assessing neoadjuvant treatment response and thereby tailoring the most appropriate therapy strategies for the patients.

Value of mammographic microcalcifications and MRI-enhanced lesions in the evaluation of residual disease after neoadjuvant therapy for breast cancer.

Microcalcifications persist even if a patient with breast cancer achieves pathologic complete response (pCR) as confirmed by surgery after neoadjuvant treatment (NAT). In practice, surgeons tend to remove all the microcalcifications. This study aimed to explore the correlation between changes in the extent of microcalcification after NAT and pathological tumor response and compare the accuracy of mammography (MG) and magnetic resonance imaging (MRI) in predicting the size of residual tumors. This was a retrospective study which included a consecutive series of patients in Guangdong Provincial People's Hospital. Between January 2010 and January 2020, 127 patients with breast cancer and Breast Imaging Reporting and Data System (BI-RADS) 4-5 microcalcifications were included in this study. The maximum diameter of the microcalcifications on MG and lesion enhancement on MRI pre- and post-NAT were measured. The correlations between the changes in residual microcalcifications on MG and pCR were analyzed. Intraclass correlation coefficients (ICCs) were computed between the extent of the residual microcalcifications, residual enhancement, and residual tumor size. There were no statistically significant differences in the changes in microcalcifications after NAT according to the RECIST criteria on MRI (P=0.09) and Miller-Payne grade (P=0.14). MRI showed a higher agreement than did residual microcalcifications on MG in predicting residual tumor size (ICC: 0.771 vs. 0.097). MRI is more accurate for evaluating residual tumor size in breast cancer. In our study, the extent of microcalcifications on MG after NAT had nearly no correlation with the pathological size of the residual tumor. Therefore, residual tumors with microcalcifications may not necessarily be a contraindication to breast-conserving surgery.

389. DEVELOPING A PROGNOSTICALLY SIGNIFICANT MORPHOLOGICAL CLASSIFICATION SYSTEM FOR ESOPHAGEAL ADENOCARCINOMA

Abstract Background Histologically, esophageal adenocarcinoma is broadly divided into two morphological subtypes: intestinal and diffuse types, with the latter conferring a worse prognosis. However, the present classification system does not account for morphological heterogeneity and the clinical and prognostic implications of this heterogeneity remains uncertain. This study focuses on elucidating the clinical and prognostic significance of the various EAC morphological patterns. Methods We identified surgical resection cases conducted at a single center from 2008 to the present date. All tumour-containing slides were analyzed, and the percentages of each morphological pattern was calculated. The overall percentage of each morphology was determined by summing across all tumour slides per case. Cases were stratified by neoadjuvant treatment status. Results This study reviewed 106 surgical resection cases. Seventy cases (58 male, 12 female, 59.26 +/− 11.12 years) received neoadjuvant treatment prior to surgery, while 36 cases (29 male, 7 female, 66.36 +/− 11.85 years) did not. A similar average slide number was obtained from treated versus untreated cases (10.13 +/− 7.43 slides vs 9.14 +/− 8.57, p = 0.5391). Morphological heterogeneity (i.e., 3+ morphologies occupying &amp;gt;10% of the tumor) was not different between treated and untreated cases (33% versus 24.2%, p = 0.35). Subgroup analysis revealed a significant increase in tumor morphological heterogeneity with poorer neoadjuvant treatment response (score 3) (p &amp;lt; 0.05). Conclusions To our knowledge, this is the first study investigating the clinical and prognostic implications of the heterogeneous morphological patterns observed in EAC. Our results suggest that tumours with poor response to neoadjuvant therapy are more likely to exhibit significant morphological heterogeneity. We are currently exploring whether specific morphological patterns are linked to adverse prognosis and poor response to neoadjuvant therapy. These findings have the potential to guide clinical decision-making in EAC.

Development and multicenter validation of a multiparametric imaging model to predict treatment response in rectal cancer

ObjectivesTo develop and validate a multiparametric model to predict neoadjuvant treatment response in rectal cancer at baseline using a heterogeneous multicenter MRI dataset.MethodsBaseline staging MRIs (T2W (T2-weighted)-MRI, diffusion-weighted imaging (DWI) / apparent diffusion coefficient (ADC)) of 509 patients (9 centres) treated with neoadjuvant chemoradiotherapy (CRT) were collected. Response was defined as (1) complete versus incomplete response, or (2) good (Mandard tumor regression grade (TRG) 1–2) versus poor response (TRG3-5). Prediction models were developed using combinations of the following variable groups:(1) Non-imaging: age/sex/tumor-location/tumor-morphology/CRT-surgery interval(2) Basic staging: cT-stage/cN-stage/mesorectal fascia involvement, derived from (2a) original staging reports, or (2b) expert re-evaluation(3) Advanced staging: variables from 2b combined with cTN-substaging/invasion depth/extramural vascular invasion/tumor length(4) Quantitative imaging: tumour volume + first-order histogram features (from T2W-MRI and DWI/ADC)Models were developed with data from 6 centers (n = 412) using logistic regression with the Least Absolute Shrinkage and Selector Operator (LASSO) feature selection, internally validated using repeated (n = 100) random hold-out validation, and externally validated using data from 3 centers (n = 97).ResultsAfter external validation, the best model (including non-imaging and advanced staging variables) achieved an area under the curve of 0.60 (95%CI=0.48–0.72) to predict complete response and 0.65 (95%CI=0.53–0.76) to predict a good response. Quantitative variables did not improve model performance. Basic staging variables consistently achieved lower performance compared to advanced staging variables.ConclusionsOverall model performance was moderate. Best results were obtained using advanced staging variables, highlighting the importance of good-quality staging according to current guidelines. Quantitative imaging features had no added value (in this heterogeneous dataset).Clinical relevance statementPredicting tumour response at baseline could aid in tailoring neoadjuvant therapies for rectal cancer. This study shows that image-based prediction models are promising, though are negatively affected by variations in staging quality and MRI acquisition, urging the need for harmonization.Key PointsThis multicenter study combining clinical information and features derived from MRI rendered disappointing performance to predict response to neoadjuvant treatment in rectal cancer.Best results were obtained with the combination of clinical baseline information and state-of-the-art image-based staging variables, highlighting the importance of good quality staging according to current guidelines and staging templates.No added value was found for quantitative imaging features in this multicenter retrospective study. This is likely related to acquisition variations, which is a major problem for feature reproducibility and thus model generalizability.

A Radiomics Model Based on Synthetic MRI Acquisition for Predicting Neoadjuvant Systemic Treatment Response in Triple-Negative Breast Cancer.

Purpose To determine if a radiomics model based on quantitative maps acquired with synthetic MRI (SyMRI) is useful for predicting neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC). Materials and Methods In this prospective study, 181 women diagnosed with stage I-III TNBC were scanned with a SyMRI sequence at baseline and at midtreatment (after four cycles of NAST), producing T1, T2, and proton density (PD) maps. Histopathologic analysis at surgery was used to determine pathologic complete response (pCR) or non-pCR status. From three-dimensional tumor contours drawn on the three maps, 310 histogram and textural features were extracted, resulting in 930 features per scan. Radiomic features were compared between pCR and non-pCR groups by using Wilcoxon rank sum test. To build a multivariable predictive model, logistic regression with elastic net regularization and cross-validation was performed for texture feature selection using 119 participants (median age, 52 years [range, 26-77 years]). An independent testing cohort of 62 participants (median age, 48 years [range, 23-74 years]) was used to evaluate and compare the models by area under the receiver operating characteristic curve (AUC). Results Univariable analysis identified 15 T1, 10 T2, and 12 PD radiomic features at midtreatment that predicted pCR with an AUC greater than 0.70 in both the training and testing cohorts. Multivariable radiomics models of maps acquired at midtreatment demonstrated superior performance over those acquired at baseline, achieving AUCs as high as 0.78 and 0.72 in the training and testing cohorts, respectively. Conclusion SyMRI-based radiomic features acquired at midtreatment are potentially useful for identifying early NAST responders in TNBC. Keywords: MR Imaging, Breast, Outcomes Analysis ClinicalTrials.gov registration no. NCT02276443 Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Houser and Rapelyea in this issue.

Registration-based biomarkers for neoadjuvant treatment response of pancreatic cancer via longitudinal image registration.

Pancreatic ductal adenocarcinoma (PDAC) frequently presents as hypo- or iso-dense masses with poor contrast delineation from surrounding parenchyma, which decreases reproducibility of manual dimensional measurements obtained during conventional radiographic assessment of treatment response. Longitudinal registration between pre- and post-treatment images may produce imaging biomarkers that more reliably quantify treatment response across serial imaging. Thirty patients who prospectively underwent a neoadjuvant chemotherapy regimen as part of a clinical trial were retrospectively analyzed in this study. Two image registration methods were applied to quantitatively assess longitudinal changes in tumor volume and tumor burden across the neoadjuvant treatment interval. Longitudinal registration errors of the pancreas were characterized, and registration-based treatment response measures were correlated to overall survival (OS) and recurrence-free survival (RFS) outcomes over 5-year follow-up. Corresponding biomarker assessments via manual tumor segmentation, the standardized response evaluation criteria in solid tumors (RECIST), and pathological examination of post-resection tissue samples were analyzed as clinical comparators. Average target registration errors were for a biomechanical image registration algorithm and for a diffeomorphic intensity-based algorithm, corresponding to 1-2 times voxel resolution. Cox proportional hazards analysis showed that registration-derived changes in tumor burden were significant predictors of OS and RFS, while none of the alternative comparators, including manual tumor segmentation, RECIST, or pathological variables were associated with consequential hazard ratios. Additional ROC analysis at 1-, 2-, 3-, and 5-year follow-up revealed that registration-derived changes in tumor burden between pre- and post-treatment imaging were better long-term predictors for OS and RFS than the clinical comparators. Volumetric changes measured by longitudinal deformable image registration may yield imaging biomarkers to discriminate neoadjuvant treatment response in ill-defined tumors characteristic of PDAC. Registration-based biomarkers may help to overcome visual limits of radiographic evaluation to improve clinical outcome prediction and inform treatment selection.

Impact of race on BluePrint genomic subtyping in HER2+ breast cancer.

564 Background: Breast cancer (BC) is the leading cause of cancer-related deaths in Black women, who are 41% more likely to die than White women. We’ve recently reported that Black women have a significantly greater proportion of hormone receptor-positive (HR+) tumors further classified as Basal-Type with BluePrint genomic subtyping, which may contribute to their worse outcomes. Here, we evaluated whether there were differences in tumor genomics among patients with clinically assessed HER2+ (cHER2) tumors between Black and White women. Methods: This study includes 204 women with stage I-III cHER2 BC who received BluePrint testing and are participants of the BEST study (5R01CA204819) at Vanderbilt University Medical Center or FLEX study (NCT03053193). Patients were treated per NCCN guidelines based on immunohistochemistry (IHC)/FISH classification. cHER2 tumors were further classified by BluePrint, an 80-gene molecular subtyping test, as Luminal-Type (gLuminal), HER2-Type (gHER2), or Basal-Type (gBasal). White women within FLEX were matched by age, tumor and node status and included as a reference group. A two-tailed proportional z-test was used to assess BluePrint subtype differences. Limma R package was used for differential gene expression analysis (DGEA) of whole transcriptome data. Significant differentially expressed genes had an adjusted p-value &lt; 0.05 and absolute log2 fold change &gt; 1. Results: Of 102 Black women with cHER2 tumors, 59 had gHER2, 34 had gLuminal and 9 had gBasal tumors, and of 102 White women, 63 had gHER2, 28 had gLuminal and 11 had gBasal tumors. There were no significant differences in the frequency of BluePrint subtypes or in gene expression by race among cHER2 tumors. All gHER2 tumors had upregulated HER2 signaling compared to upregulated estrogen signaling genes in gLuminal tumors, and upregulated genes characteristic of metastasis and triple negative tumors in gBasal tumors. Among patients with available neoadjuvant treatment response data, 57.1% (16/28) gHER2, 33.3% (2/6) gLuminal and 50.0% (3/6) gBasal achieved a pathologic complete response (pCR). Conclusions: Unlike HR+HER2- tumors, these data suggest that race may not influence the biology underlying cHER2 tumors. However, there was genomic heterogeneity among cHER2 tumors identified by BluePrint, independent of race. Overall, the rate of BluePrint classification among cHER2 tumors was comparable to NBRST and APHINITY, which demonstrated distinct treatment response and outcome based on BluePrint genomic subtype. In this study, DGEA suggests gHER2 tumors exhibit extensive HER2 signaling and may benefit most from HER2-targeted therapy. Although limited by sample size, rates of pCR to neoadjuvant therapy appear greater in gHER2 than non-HER2 tumors (gLuminal, gBasal). gBasal tumors may harbor more aggressive tumor characteristics, which has important clinical implications for optimizing treatment and improving outcomes in these patients.

Pathologic complete response (pCR) to neoadjuvant treatment (NAT) with carboplatin, nab-paclitaxel and anthracycline in early and locally advanced triple-negative breast cancer (TNBC).

e12608 Background: Achieving a pCR (absence of residual invasive disease in breast and measurable disease in any of the axillary nodes sampled, ypT0/is and ypN0) after NAT correlates with improved survival. TNBC phenotype have a poor prognosis. However, it is associated with higher NAT response rates. With traditional schemes based on anthracyclines and taxanes, pCR rates of around 25-40% are obtained. Different schemes have been studied providing an increase in pCR such as using nab-paclitaxel, platinum salts, bevacizumab or dose dense (dd) anthracyclines. The purpose of the present study is to explore feasibility, toxicity and pCR of new aproach with carboplatine, nab-paclitaxel and antracycline in TNBC. Methods: Retrospective study with early and locally advanced TNBC pts, ECOG 0-1, without contraindications to antracyclines, were included. Treatment consisted of carboplatine AUC2 with nab-paclitaxel 80 mg/m2 i.v. weekly for 12 doses followed by epirubicin 90 mg/m2 (elderly pts recieved at 75 mg/m2) with cyclophosphamide 600 mg/m2 i.v., every 2 weeks (dd) or 3 weeks (if pts had G3-4 toxicity) for 4 doses. G-CSF support was allowed in pts with neutropenia G3-4. Pts with residual disease after NAT were offered capecitabine as adjuvant treatment. The primary endpoint was pCR. Results: From 02/2018 to 06/2022, 96 pts was included. Median age was 53 years (27-78). Clinical stage was I in 16 pts, II in 50 pts and III in 30 pts (N+ in 40'6%). 98% pts had tumours with a Ki67 20% or higher. 36’5% pts was premenopausal. 10 pts were germline BRCA mutated (8/10 gBRCA1, 2/10 gBRCA2) 50/96 pts (55’2%) had dd epirubicin-cyclophospamide (EC) and 44’8% pts recieved EC every 3 weeks (for G3 adverse events (AE)). pCR in pts who had dd treatment was 65’4% and 40’9% in pts with EC non-dd (p 0’016). 89’9% pts with residual disease completed adjuvant treatment with 8 cycles of capecitabine. 53/96 pts had an AE related with NAT. Neutropenia G3-4 (45’5% pts, all of them recieved dd treatment) and any grade of alopecia and mucositis (60% and 25% respectively) were the most frecuent AE reported. Pts with neutropenia G3-4 on blood test recieved G-CSF in subsequent cycles with resolution of haematological AE. All pts with dd treatment recieved G-CSF. One patient had a disease progression by radiological evaluation during the treatment. During follow-up 9 pts relapsed (1 had a pCR with NAT) and 5 pts died for progression disease. Conclusions: NAT on TNBC pts with carboplatine, nab-paclitaxel and antracycline dd is a feasible, safe and highly effective option. Even though survival analysis is not yet mature in our study, pCR has been shown to be a surrogate for overall survival. The excellent results obtained with this scheme, make it a good treatment option and could be an alternative to pembrolizumab (Keynote-522) in pts with autoinmmune diseases or immune-AE due to similar pCR.

Abstract 4539: Tumor evolution and immune microenvironment dynamics define response to neoadjuvant treatment of esophageal adenocarcinoma

Abstract Introduction: Locally advanced esophageal adenocarcinoma (EAC) remains difficult to treat, and resistance is common. The ecological and evolutionary dynamics responsible for treatment failure are incompletely understood. Aim &amp; Methods: We performed a multi-omic study with a multi-timepoint strategy to examine neoadjuvant treatment response at clonal resolution and in the surrounding tumor microenvironment. EAC samples from chemotherapy responding (REs) and chemotherapy non-responding (NRs) patients with locally advanced EAC were collected at three time points (prior, during, and after neoadjuvant therapy) within the multicenter MEMORI trial. Whole exome sequencing (mean depth 300x) was performed on 47 samples from 17 REs and on 24 samples from 10 NRs. Matched RNA sequencing was performed on 53 samples from 17 REs and on 26 samples from 10 NRs. To characterize immune response we performed imaging mass cytometry (IMC) with a 18-marker panel on 26 samples from 9 REs and 16 samples from 6 NRs and T-cell receptor sequencing in 18 RE-samples and 9 NR-samples. Results: We observed profound changes in mutation signatures over time, characterized by C&amp;gt;A transitions after exposure to neoadjuvant chemotherapy (FOLFOX), which is consistent with earlier observed oxaliplatin induced mutational signatures. Phylogenetic analysis showed no major changes in the clonal make-up during treatment, suggesting phenotypic plasticity rather than clonal evolution caused treatment resistance. EAC samples displayed widespread copy number alterations (CNAs), as previously described. CNAs arising during treatment were significantly more likely to be small, focal alterations rather than large alterations. At the transcriptome level neoadjuvant treatment led to significant changes with significant upregulation of immune and stromal pathways and oncogenic pathways such as KRAS, Hedgehog and WNT. The presence of immune escape mechanisms (defined as LOH or mutations in HLA, B2M mutations or PDL-1 overexpression) and a lack of clonal T-cell expansions were linked to poor clinical treatment response. Moreover, IMC analyses showed a less activated T-cell phenotype in NRs than in REs throughout treatment. Conclusion: Using a multi-timepoint approach, we integrated genetic analyses with transcriptomic analyses and analyses of the tumor immune microenvironment for a holistic understanding of treatment resistance. This study identifies profound transcriptional changes during treatment with limited evidence that clonal replacement is the cause, suggesting phenotypic plasticity as a mechanism for therapy resistance. The presence of immune escape, a less activated T-cell phenotype and a lack of clonal T-cell expansions in patients with poor clinical treatment response has high pharmacological relevance and could be exploited via combined immune-chemotherapy treatments. Citation Format: Melissa Barroux, Jacob Househam, Eszter Lakatos, Tahel Ronel, Ann-Marie Baker, Henrike Salié, Max Mossner, Kane Smith, Chris Kimberley, Salpie Nowinski, Alison Berner, Vinaya Gunasri, Marnix Jansen, Giulio Caravagna, Julia Slotta-Huspenina, Wilko Weichert, Markus Alberstmeier, Benny Chain, Helmut Friess, Bertram Bengsch, Roland M. Schmid, Jens T. Siveke, Michael Quante, Trevor A. Graham. Tumor evolution and immune microenvironment dynamics define response to neoadjuvant treatment of esophageal adenocarcinoma. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4539.