Pathologic Complete Response In Patients Research Articles

Abstract PO4-03-07: The impact of chemotherapy relative dose intensity on pathological complete response in patients with triple negative breast cancer treated with neoadjuvant chemotherapy

Abstract Background: Neoadjuvant chemotherapy (NACT) is the preferred treatment approach for early-stage triple negative breast cancer (TNBC). Achieving pathologic complete response (pCR) after NACT is associated with improved event-free survival and overall survival in patients with TNBC. Major research efforts have been deployed to identify predictive biomarkers for pCR in TNBC. However, the values of chemotherapy (CT) relative dose intensity (RDI) in predicting pCR remain unclear. Methods: A retrospective analysis was conducted among 96 consecutive patients with early stage TNBC who received NACT between February 2018 and January 2023 at the University of Naples Federico II (Naples, Italy). Patients’ demographics, clinical-pathological features, and type of CT schedules were retrieved from electronic medical records. pCR was defined as the absence of residual invasive or in situ carcinoma in primary tumor and/or lymph nodes, upon chemotherapy administration. RDI was calculated as the ratio of delivered to planned CT dose intensity. RDI was defined as low if < 85% or high if ≥85%, respectively. Univariate and multivariate logistic regression were used to evaluate the association between age, tumor stage, RDI, CT regimen dose type, and pCR. Results: All patients were included in the analysis. Median age at diagnosis was 51 years (range 28-75). Sixty-five (68%) and 31 (32%) patients were diagnosed with stage II and stage III TNBC, respectively. Nineteen (19%) patients received a sequential schedule of anthracycline plus cyclophosphamide (AC) and a taxane (T). Sixty-one (64%) patients received AC followed by concurrent carboplatin-paclitaxel (CaP). Sixteen (17%) patients received the anti-PD1 pembrolizumab in addition to ACCaP chemotherapy backbone (ACCaP-Pem). Fifty-four (56%) patients received a dose-dense AC-based CT. A pCR was achieved in 59 (61%) patients. Interestingly, patients who received a dose-dense AC based neoadjuvant CT had higher chance to achieve a pCR compared with those treated with a non dose-dense AC based CT (70% vs 46%, p = 0.03). Overall, 79 (82%) and 17 (18%) patients received a RDI high and RDI low CT, respectively. No difference in terms of age, body mass index, performance status, tumor stage, type of chemotherapy was observed between the two groups (Table 1). At univariate analysis RDI high (OR 5.18, 95%CI: 1.65-16.31, p=0.005) and dose dense AC-based CT (OR 2.42, 95%CI: 0.18-0.97, p=0.04) were significantly associated with pCR. In a multivariate model including RDI, age, tumor stage, and AC regimen dose type, RDI was the only variable independently associated with pCR (OR 4.60, 95% CI: 1.40-15.06, p = 0.01). Conclusion: A chemotherapy RDI≥ 85% independently predicts pCR in early stage TNBC patients treated with standard NACT. In our cohort, one in five patients received a suboptimal chemotherapy dose intensity affecting at least in part tumor response. Studies with larger TNBC patient populations are warranted to confirm the impact of RDI on pCR and long-term outcomes. Table 1 Citation Format: Roberto Buonaiuto, Giuseppe Neola, Aldo Caltavituro, Paola Trasacco, Federica Pia Mangiacotti, Giovanna Pecoraro, Matteo Lambertini, Erica Pietroluongo, Pietro De Placido, Sabino De Placido, Valeria Forestieri, Mario Giuliano, Grazia Arpino, Carmine De Angelis. The impact of chemotherapy relative dose intensity on pathological complete response in patients with triple negative breast cancer treated with neoadjuvant chemotherapy [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-03-07.

Abstract 6203: An altered profile of ribosomal proteins and ERK-eIF4E translational control in an inflammatory breast cancer model of acquired resistance and reversal is associated with pathological complete response in patients

Abstract Background: Drug resistance is a major challenge in inflammatory breast cancer (IBC), a hyperproliferative subtype characterized by the presence of diffuse tumor cell clusters that show high level of heterogeneity. As the degree of tumor heterogeneity during cancer progression can cause evolution of cell states that can either compete or co-operate resulting in therapeutic failure, the study objective was to identify drug resistance networks that can serve as biomarkers or therapeutic targets. Methods: Integrated transcriptomic and proteomic profiling approach was conducted in a progression model of acquired drug resistance and its reversal comprised of a treatment-naïve patient-derived SUM149 cell line, along with two isogenic derivatives and representing drug-resistant (rSUM149) and resistance-reversal states (rrSUM149). Global gene expression analysis in IBC patients (n=87) recorded responses to neoadjuvant chemotherapy. Based on the data, targeted multikinase inhibitors were evaluated in viability and signaling assays. Results: We identified specific ribosomal proteins associated with resistance acquisition, which correlated with high levels of pERK, CDK1, XIAP, and SOD2. Conversely, the resistance reversal in rrSUM149 showed almost complete normalization to the profile of SUM149. However, VIPER analysis revealed proteins related to ribosomal processes (AGO2, Exportin 1, RPL5) that did not revert in rrSUM149, suggesting this pathway may have pleiotropic effects in governing drug resistance. Notably, genes linked to ribosomal processes were significantly enriched (P<0.001) among the overexpressed genes in IBC patients (n=87) who exhibited pCR to neoadjuvant chemotherapy. This is crucial as IBC tumors, including rSUM149, exhibit MAPK hyperactivation including in the presence of EGFR tyrosine kinase inhibitors. Treatment with Merestinib, a multikinase inhibitor, suppressed pERK, peIF4E, downstream targets like CDK1, and ribosomal protein IMP3 underscoring the impact on protein synthesis signaling crucial for countering translational dependence in cancer cells. Additionally, Merestinib synergized with EGFR-TKI (lapatinib) in increasing cell death in all three states. Conclusion: Overall, our study describes adaptive changes in response to both therapeutic stress and therapy break that have the potential to serve as biomarkers or extended for pharmacological interrogation toward precision medicine for this rare, understudied cancer. Furthermore, the ability of Merestinib, in clinical trials, to target translational dependence is attractive as cancer cells often appropriate these signaling pathways to gain tolerance to various stress stimuli. Support: NIH-R01CA264529; DoD Breast Cancer Breakthrough level 2 Award W81XWH2010153 Citation Format: Gayathri R. Devi, Pritha PAI, Matthew W. Foster, Dorababu S. Sannareddy, Seayoung Lee, Steven V. Laere. An altered profile of ribosomal proteins and ERK-eIF4E translational control in an inflammatory breast cancer model of acquired resistance and reversal is associated with pathological complete response in patients [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 6203.

Correlation between trophoblast cell-surface antigen-2 (Trop-2) expression and pathological complete response in patients with HER2-positive early breast cancer treated with neoadjuvant docetaxel, carboplatin, trastuzumab, and pertuzumab.

The prognostic and predictive role of trophoblast cell-surface antigen-2 (Trop-2) overexpression in human epidermal growth factor receptor 2-positive (HER2-positive) breast cancer is currently unknown. We retrospectively analyzed Trop-2 expression and its correlation with clinicopathologic features and pathological complete response (pCR) in HER2-positive early breast cancer (EBC) patients treated with neoadjuvant docetaxel, carboplatin, trastuzumab, and pertuzumab in the PHERGain study. Trop-2 expression at baseline was determined in formalin-fixed, paraffin-embedded primary tumor biopsies by immunohistochemistry and was first classified into expressing (Trop-2-positive) or not-expressing (Trop-2-negative) tumors. Then, it was classified by histochemical score (H-score) according to its intensity into low (0-9), intermediate (10-49), and high (≥ 50). The association between clinicopathologic features, pCR, and Trop-2 expression was performed with Fisher's exact test. Forty-one patients with tissue evaluable for Trop-2 expression were included, with 28 (68.3%) Trop-2-positive tumors. Overall, 17 (41.46%), 14 (34.15%), and 10 (24.40%) tumors were classified as low, intermediate, and high, respectively. Trop-2 expression was significantly associated with decreased pCR rates (50.0% vs. 92.3%; odds ratio [OR] 0.05; 95% CI, 0.002-0.360]; p adjusted = 0.01) but was not correlated with any clinicopathologic features (p ≥ 0.05). Tumors with the highest Trop-2H-score were less likely to obtain a pCR (OR 0.03; 95% CI, 0.001-0.290, p adjusted < 0.01). This association was confirmed in univariate and multivariate regression analyses. These findings suggest a potential role of Trop-2 expression as a biomarker of resistance to neoadjuvant chemotherapy plus dual HER2 blockade and may become a strategic target for future combinations in HER2-positive EBC patients.

Pathologic complete response in patients with esophageal cancer receiving neoadjuvant chemotherapy or chemoradiation: A systematic review and meta-analysis.

Neoadjuvant chemoradiation and chemotherapy are recommended for the treatment of nonmetastatic esophageal cancer. The benefit of neoadjuvant treatment is mostly limited to patients who exhibit pathologic complete response (pCR). Existing estimates of pCR rates among patients receiving neoadjuvant therapy have not been synthesized and lack precision. We conducted an independently funded systematic review and meta-analysis (PROSPERO CRD42023397402) of pCR rates among patients diagnosed with esophageal cancer treated with neoadjuvant chemo(radiation). Studies were identified from Medline, EMBASE, and CENTRAL database searches. Eligible studies included trials published from 1992 to 2022 that focused on nonmetastatic esophageal cancer, including the gastroesophageal junction. Histology-specific pooled pCR prevalence was determined using the Freeman-Tukey transformation and a random effects model. After eligibility assessment, 84 studies with 6451 patients were included. The pooled prevalence of pCR after neoadjuvant chemotherapy in squamous cell carcinomas was 9% (95% CI: 6%-14%), ranging from 0% to 32%. The pooled prevalence of pCR after neoadjuvant chemoradiation in squamous cell carcinomas was 32% (95% CI: 26%-39%), ranging from 8% to 66%. For adenocarcinoma, the pooled prevalence of pCR was 6% (95% CI: 1%-12%) after neoadjuvant chemotherapy, and 22% (18%-26%) after neoadjuvant chemoradiation. Under one-third of patients with esophageal cancer who receive neoadjuvant chemo(radiation) experience pCR. Patients diagnosed with squamous cell carcinomas had higher rates of pCR than those with adenocarcinomas. As pCR represents an increasingly utilized endpoint in neoadjuvant trials, these estimates of pooled pCR rates may serve as an important benchmark for future trial design.

Higher rate of pathologic complete response in patients with early-onset locally advanced rectal cancer

Higher rate of pathologic complete response in patients with early-onset locally advanced rectal cancer

The predictive value of systemic immune-inflammatory markers before and after treatment for pathological complete response in patients undergoing neoadjuvant therapy for breast cancer: a retrospective study of 1994 patients.

Systemic immune-inflammatory markers have a certain predictive role in pathological complete response (pCR) after neoadjuvant treatment (NAT) in breast cancer. However, there is a lack of research exploring the predictive value of markers after treatment. This retrospective study collected data from 1994 breast cancer patients who underwent NAT. Relevant clinical and pathological characteristics were included, and pre- and post-treatment complete blood cell counts were evaluated to calculate four systemic immune-inflammatory markers: neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic immune-inflammation index (SII). The optimal cutoff values for these markers were determined using ROC curves, and patients were classified into high-value and low-value groups based on these cutoff values. Univariate and multivariate logistic regression analyses were conducted to analyze factors influencing pCR. The factors with independent predictive value were used to construct a nomogram. After NAT, 383 (19.2%) patients achieved pCR. The area under the ROC curve is generally larger for post-treatment markers compared to pre-treatment markers. Pre-treatment NLR and PLR, as well as post-treatment LMR and SII, were identified as independent predictive factors for pCR, along with Ki-67, clinical tumor stage, clinical lymph node stage, molecular subtype, and clinical response. Higher pre-NLR (OR = 1.320; 95% CI 1.016-1.716; P = 0.038), pre-PLR (OR = 1.474; 95% CI 1.058-2.052; P = 0.022), post-LMR (OR = 1.532; 95% CI 1.175-1.996; P = 0.002), and lower post-SII (OR = 0.596; 95% CI 0.429-0.827; P = 0.002) are associated with a higher likelihood of achieving pCR. The established nomogram had a good predictive performance with an area under the ROC curve of 0.754 (95% CI 0.674-0.835). Both pre- and post-treatment systemic immune-inflammatory markers have a significant predictive role in achieving pCR after NAT in breast cancer patients. Indeed, it is possible that post-treatment markers have stronger predictive ability compared to pre-treatment markers.

Assessment of novel prognostic biomarkers to predict pathological complete response in patients with non-metastatic triple-negative breast cancer using a window of opportunity design.

Triple-negative breast cancer (TNBC) includes approximately 20% of all breast cancer and is characterized by its aggressive nature, high recurrence rates, and visceral metastasis. Pathological complete response (pCR) is an established surrogate endpoint for survival. The window of opportunity studies provide valuable information on the disease biology prior to definitive treatment. To study the association of dynamic change in pathological, imagining, and genomic biomarkers that can prognosticate pCR. The study aims to develop a composite prognostic score. Clinical, interventional, and prognostic biomarker study using the novel window of opportunity design. The study aims to enroll 80 treatment-naïve, pathologically confirmed TNBC patients, administering a single dose of paclitaxel and carboplatin during the window period before neoadjuvant chemotherapy (NACT). Tumor tissue will be obtained through a tru-cut biopsy, and positron emission tomography and computed tomography scans will be performed for each patient at two time points aiming to evaluate biomarker alterations. This will be followed by the administration of standard dose-dense NACT containing anthracyclines and taxanes, with the study culminating in surgery to assess pCR. The study would develop a composite prognostic risk score derived from the dynamic change in the Ki-67, tumor-infiltrating lymphocytes, Standardized Uptake Value (SUV max), Standardized Uptake Value for lean body mass (SUL max), and gene expression level pre- and post-intervention during the window period prior to the start of definitive treatment. This outcome will aid in categorizing the disease biology into risk categories. The current study is approved by the Institutional Ethics Committee [Ethics: Protocol. no. JIP/IEC/2020/019]. This study was registered with ClinicalTrials.gov [CTRI Registration: CTRI/2022/06/043109]. The validated biomarker score will help to personalize NACT protocols in patients in TNBC planned for definitive treatment.

Development and validation of a nomogram to predict pathological complete response in patients with locally advanced gastric adenocarcinoma treated with neoadjuvant chemotherapy in combination with PD-1 antibodies.

Currently, the survival benefits of combining neoadjuvant chemotherapy with programmed death 1 (PD-1) antibody immunotherapy in advanced gastric adenocarcinoma remain controversial. Emerging evidence suggests that the survival benefits of neoadjuvant therapy in advanced gastric adenocarcinoma hinge upon the attainment of pathological complete response (pCR). Therefore, the prediction of pCR in patients undergoing neoadjuvant chemotherapy combined with PD-1 antibody immunotherapy holds significant importance and is beneficial for the individualized treatment of gastric cancer (GC) patients. Clinical and pathological characteristics of patients with GC who received neoadjuvant chemotherapy combined with PD-1 inhibitor (camrelizumab) therapy and radical gastrectomy between January 2019 and December 2020 at the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital were retrospectively analyzed. A total of 52 patients were enrolled in the study, with all subjects assigned to the training set. The neoadjuvant regimen consisted of a combination of PD-1 inhibitor and fluorouracil analogues plus oxaliplatin, comprising two drugs. The patients were divided into a pCR group and a non-pCR group according to pCR occurrence. Multifactor logistic regression analysis was applied to determine the correlation between each factor and pCR. A prediction model was developed based on the results of the logistic regression analysis. The predictive performance of the model was evaluated using the receiver operating characteristic curves. Internal verification was completed via the bootstrapping method. The pCR was observed in 10 out of 52 patients (19.2%). The results of binary logistic regression multivariate analysis showed that cN stage [odds ratio (OR): 0.215; P=0.03], combined positive score (CPS) (OR: 6.364; P=0.026), and tumor diameter (OR: 0.112; P=0.026) were independent predictors of pCR. The nomogram prediction model for the pCR was plotted with a concordance index of 0.923 [95% confidence interval (CI): 0.8441-1]. Neoadjuvant chemotherapy combined with PD-1 antibodies may be the preferred option for patients with advanced gastric adenocarcinoma who have a small tumor diameter, no or few lymph node metastases, and high CPS. The presented nomogram model exhibits the potential to predict pCR in advanced gastric adenocarcinoma patients, showcasing satisfactory predictive performance and potentially facilitating the implementation of personalized treatment strategies.

Emergence of immune-related adverse events correlates with pathological complete response in patients receiving pembrolizumab for early triple-negative breast cancer

ABSTRACT Based upon results of the KEYNOTE-522 trial and following approval by regulatory authorities, the addition of pembrolizumab to chemotherapy is now the standard-of-care for the treatment of early triple-negative breast cancer (eTNBC) (Clinical stage II-III). Pembrolizumab is a programmed cell death protein 1 monoclonal antibody, known to cause immune-related adverse events (irAEs) in a significant subset of patients. Real-world data on incidence, type and treatment strategies of irAEs in the setting of eTNBC treatment are sparse. In this multicenterretrospective analysis, we characterized real-world incidence of irAEs and treatment outcomes such as pathological complete response (pCR) from the combination of pembrolizumab and chemotherapy as neoadjuvant treatment for eTNBC. We found a rate of irAEs of all grades of 63.9% and of 20% for irAEs of grade 3 or higher. In the overall population, a pCR rate of 57.1% was observed. The emergence of irAEs correlated significantly with pCR (72.2% versus 30.8%; p =.03). Discontinuation of neoadjuvant chemotherapy before week 12 correlated significantly with a lower pCR rate. To our knowledge, this is the first study evaluating the real-world efficacy and safety of a neoadjuvant combination of chemotherapy and pembrolizumab in eTNBC, demonstrating a significant correlation between irAEs and pCR. Early discontinuation of neoadjuvant therapy due to AEs resulted in a lower pCR rate.

Complete pathological response in patients with HER2 positive breast cancer treated with neoadjuvant therapy in Colombia

Breast cancer is the most common type of cancer and the leading cause of death by cancer in women in Colombia. Approximately 15 to 20% of breast cancers overexpress HER2. To analyze the relationship between multiple clinical and histological variables and pathological complete response in patients with HER2-positive breast cancer undergoing neoadjuvant therapy in a specialized cancer center in Colombia. We performed a retrospective analysis of non-metastatic HER2-positive breast cancer patients who received neoadjuvant therapy between 2007 and 2020 at the Instituto de Cancerología Las Americas Auna (Medellín, Colombia). Assessed parameters were tumor grade, proliferation index, estrogen receptor, progesterone receptor, HER2 status, type of neoadjuvant therapy, pathologic complete response rates, and overall survival. Variables associated with low pathologic complete response rates were tumor grades 1-2 (OR = 0.55; 95% CI = 0.37-0.81; p = 0.03), estrogen receptor positivity (OR =0.65; 95%; CI = 0.43-0.97; p=0.04), and progesterone receptor positivity (OR = 0.44; 95% CI = 0.29-0.65; p = 0.0001). HER2 strong positivity (score 3+) was associated with high pathological complete response rates (OR = 3.3; 95% CI = 1.3-8.35; p=0.013). Five-year overall survival was 91.5% (95% CI = 82.6-95.9) in patients with pathological complete response and 73.6% (95% CI = 66.4-79.6) in patients who did not achieve pathological complete response (p = 0.001). Additionally, the pathological complete response rate was three times higher in patients receiving combined neoadjuvant chemotherapy with anti-HER2 therapy than in those with chemotherapy alone (48% versus 16%). In patients with HER2-positive breast cancer, tumor grade 3, estrogen receptor negativity, progesterone receptor negativity, strong HER2 positivity (score 3+), and the use of the neoadjuvant trastuzumab are associated with higher pathological complete response rates.

Using clinical and radiomic feature-based machine learning models to predict pathological complete response in patients with esophageal squamous cell carcinoma receiving neoadjuvant chemoradiation.

This study aimed to build radiomic feature-based machine learning models to predict pathological clinical response (pCR) of neoadjuvant chemoradiation therapy (nCRT) for esophageal squamous cell carcinoma (ESCC) patients. A total of 112 ESCC patients who underwent nCRT followed by surgical treatment from January 2008 to December 2018 were recruited. According to pCR status (no visible cancer cells in primary cancer lesion), patients were categorized into primary cancer lesion pCR (ppCR) group (N = 65) and non-ppCR group (N = 47). Patients were also categorized into total pCR (tpCR) group (N = 48) and non-tpCR group (N = 64) according to tpCR status (no visible cancer cells in primary cancer lesion or lymph nodes). Radiomic features of pretreatment CT images were extracted, feature selection was performed, machine learning models were trained to predict ppCR and tpCR, respectively. A total of 620 radiomic features were extracted. For ppCR prediction models, radiomic model had an area under the curve (AUC) of 0.817 (95% CI: 0.732-0.896) in the testing set; and the combination model that included rad-score and clinical features had a great predicting performance, with an AUC of 0.891 (95% CI: 0.823-0.950) in the testing set. For tpCR prediction models, radiomic model had an AUC of 0.713 (95% CI: 0.613-0.808) in the testing set; and the combination model also had a great predicting performance, with an AUC of 0.814 (95% CI: 0.728-0.881) in the testing set. This study built machine learning models for predicting ppCR and tpCR of ESCC patients with favorable predicting performance respectively, which aided treatment plan optimization. This study significantly improved the predictive value of machine learning models based on radiomic features to accurately predict response to therapy of esophageal squamous cell carcinoma patients after neoadjuvant chemoradiation therapy, providing guidance for further treatment. • Combination model that included rad-score and clinical features had a great predicting performance. • Primary tumor pCR predicting models exhibit better predicting performance compared to corresponding total pCR predicting models.

A new prognostic index associated with pathological complete response in rectal cancer

Purpose: Approximately half of rectal cancer cases are diagnosed at a locally advanced stage. It is important to identify biomarkers that can predict pathological complete response in patients undergoing surgery following neoadjuvant chemoradiotherapy.&#x0D; Materials and Methods: This retrospective study included 205 patients with locally advanced rectal cancer who underwent surgery and adjuvant chemotherapy following neoadjuvant chemoradiotherapy. Inflammatory biomarkers were assayed in the complete blood count before neoadjuvant therapy.&#x0D; Results: A pathological complete response was detected in 20.5% of the patients. The neutrophil-lymphocyte ratio, platelet-lymphocyte ratio, and pan-immune inflammation value were significantly lower in the pathological complete response (+) group than in the pathological complete response (-) group. The cut-off of the pan-immune inflammation value was ≤ 331.2, and this parameter had the best diagnostic performance of 90.4%.&#x0D; Conclusion: Neoadjuvant chemoradiotherapy followed by surgery and adjuvant chemotherapy remains the standard treatment approach for rectal cancer. Since pathological complete response improves oncological outcomes, it is important to identify biomarkers that can predict pathological complete response in rectal cancer

Trimodality Therapy for Esophageal &amp; GEJ Adenocarcinoma

Esophageal Cancer: Esophageal CancerRoughly one-fourth of patients with esophageal and gastroesophageal junction adenocarcinoma will undergo a pathological complete response. “Trimodality therapy refers to neoadjuvant chemoradiotherapy, followed by radical surgery,” noted Kohei Yamashita, PhD, at The University of Texas MD Anderson Cancer Center. At the AACR Annual Meeting 2023, researchers presented data from a study that sought to develop a model for predicting which patients would experience a pathological complete response after trimodality therapy for their esophageal and gastroesophageal junction adenocarcinoma (Abstract 951). When asked about the reasons for performing this study, Yamashita replied, “Generally, patients who achieved pathological complete response show better prognosis. Predicting treatment response prior to the therapy can be useful in clinical practice.” The ability to predict whether a patient would be likely to achieve a pathological complete response could provide important strategic guidance regarding surgical interventions. Setting this study apart from previous ones was the use of machine learning models; prior studies utilized methodologies including logistic regression models. When asked why this study focused on trimodality therapy, Yamashita explained, “Most patients are treated with trimodality therapy according to The National Comprehensive Cancer Network guidelines for esophageal and gastroesophageal junction cancers, which is based on results from Phase III clinical trials.” He also explained how pathological complete response was defined in their study. “We used the common definition of pathological complete response (ypT0N0M0). That is the lack of all signs of cancer in tissue samples removed during surgery after trimodality. A pathologist checks the tissue samples under a microscope to see if there are still cancer cells left, and reports no residual cancer cells identified.” Study Details This study included 569 patients with esophageal and gastroesophageal junction adenocarcinoma treated with trimodality therapy at MD Anderson Cancer Center between 2002 and 2022. The medical records of these patients were mined for different clinical data, including age, sex, histology, baseline TNM (tumor-node-metastasis) staging post-chemoradiotherapy, and maximized standard uptake value in 18-F-fluorodeoxyglucose PET/CT before and after treatment. The mined clinical data were then used to “train” a number of machine learning models, including BART, logistic regression, LASSO, XGboost, and Random Forest. Once trained, the methods were then validated using a “10-fold cross-validation” process. Commenting on their important findings, Yamashita noted, “We established the clinical predictive model for pathological complete response in patients with esophageal and gastroesophageal junction adenocarcinoma treated by trimodality therapy using cutting-edge machine learning methods with acceptable predictive ability. The absence of cancer on re-biopsy tissue after neoadjuvant chemoradiotherapy was most highly associated with pathological complete response, which has been well established. “However, we would like to emphasize that other characteristics, including signet ring cell component, poorly differentiated histological type, and deeper tumor depths were also associated with non-pathological complete response. The combination of various relevant variables is needed to make a good predictive model.” Assessing how this study would affect patient treatment, Yamashita observed, “An accurate model can help implement an organ preservation strategy, meaning we can avoid surgery in some patients based on model predictions.” Regarding future directions for their research, he said, “As we refine our predictive model for publication, we are discussing which clinical information to include and whether tissue-based information can be incorporated to further improve predictive capabilities. We believe that this concept of study will contribute to patient quality of life for organ preservation along with oncological safety.” Richard Simoneaux is a contributing writer.

Identifying determinants of pathologic complete response in patients with breast cancer after receiving neoadjuvant systemic therapy.

e12622 Background: In locally advanced breast cancer, neoadjuvant systemic therapy aims to downstage the disease, clear micrometastasis, assess chemosensitivity, and allow for breast conserving surgery. The attainment of a Pathologic Complete Response (pCR) has been associated with improved event free survival (EFS) and improved overall survival (OS) among various studies. We aim to identify clinicopathological factors which may influence pCR. Methods: This is a single center retrospective study of patients diagnosed with locally advanced breast cancer from 2017-2022 who received neoadjuvant therapy and investigating predictors of pCR in this subset of patients. Descriptive statistics were used to summarize the baseline characteristic variables. Variables relating to pCR were analyzed by binary logistic regression and chi-square test. A p-value of &lt; 0.05 was considered statistically significant. Results: Of the 165 patients included, all were female, with a mean age of 50 and majority (97%) with invasive ductal carcinoma (IDC) histology. Baseline characteristics are summarized. 52 had hormone (HR)pos/Her2neg, 51 with triple negative, 47 had HR pos/Her2pos, and 15 had HR neg/Her2pos disease. pCR was seen in 36% of patients. A normal BMI of &lt; 25 (p=0.03), nonsmoker (p=0.01), grade III (p=0.01), HR pos/Her2pos (p=0.02), and HR neg/Her2pos (p=0.0001), were associated with pCR. In patients with triple negative breast cancer, the use of immunotherapy did not influence pCR (p = 0.0565). There were no differences based on age, race, menopausal status, stage, size, presence of LVI or DCIS or multifocal disease. In the 157 patients where Ki-67 was known, tumors with higher Ki-67 values of 30-49% and &gt; 50% achieved pCR compared to those with lower Ki-67 (p = 0.002). Conclusions: Those with normal BMI, non-smokers, and aggressive clinicopathological factors such as Her2 positive, higher Ki67 (30% and above), and grade III disease were more likely to achieve pCR after neoadjuvant systemic therapy. These findings further identify tumor characteristics most likely to respond to neoadjuvant chemotherapy. [Table: see text]

Clinical Meaning of Stromal Tumor Infiltrating Lymphocytes (sTIL) in Early Luminal B Breast Cancer

Luminal breast cancer (BC) is associated with less immune activation, and the significance of stromal lymphocytic infiltration (sTIL) is more uncertain than in other BC subtypes. The aim of this study was to investigate the predictive and prognostic value of sTIL in early luminal BC. The study was performed with an observational design in a prospective cohort of 345 patients with predominantly high-risk luminal (hormone receptor positive, HER2 negative) BC and with luminal B features (n = 286), in which the presence of sTIL was analyzed with validated methods. Median sTIL infiltration was 5% (Q1–Q3 range (IQR), 0–10). We found that sTIL were associated with characteristics of higher biological and clinical aggressiveness (tumor and lymph node proliferation and stage, among others) and that the percentage of sTIL was predictive of pathologic complete response in patients treated with neoadjuvant chemotherapy (OR: 1.05, 95%CI 1.02–1.09, p &lt; 0.001). The inclusion of sTIL (any level of lymphocytic infiltration: sTIL &gt; 0%) in Cox regression multivariable prognostic models was associated with a shorter relapse-free interval (HR: 4.85, 95%CI 1.33–17.65, p = 0.016) and significantly improved its performance. The prognostic impact of sTIL was independent of other clinical and pathological variables and was mainly driven by its relevance in luminal B BC.

Develop and validate a radiomics space-time model to predict the pathological complete response in patients undergoing neoadjuvant treatment of rectal cancer: an artificial intelligence model study based on machine learning

ObjectiveIn this study, we aimed to investigate the predictive efficacy of magnetic resonance imaging (MRI) radiomics features at different time points of neoadjuvant therapy for rectal cancer in patients with pathological complete response (pCR). Furthermore, we aimed to develop and validate a radiomics space–time model (RSTM) using machine learning for artificial intelligence interventions in predicting pCR in patients.MethodsClinical and imaging data of 83 rectal cancer patients were retrospectively analyzed, and the patients were classified as pCR and non-pCR patients according to their postoperative pathological results. All patients received one MRI examination before and after neoadjuvant therapy to extract radiomics features, including pre-treatment, post-treatment, and delta features. Delta features were defined by the ratio of the difference between the pre- and the post-treatment features to the pre-treatment feature. After feature dimensionality reduction based on the above three feature types, the RSTM was constructed using machine learning methods, and its performance was evaluated using the area under the curve (AUC).ResultsThe AUC values of the individual basic models constructed by pre-treatment, post-treatment, and delta features were 0.771, 0.681, and 0.871, respectively. Their sensitivity values were 0.727, 0.864, and 0.909, respectively, and their specificity values were 0.803, 0.492, and 0.656, respectively. The AUC, sensitivity, and specificity values of the combined basic model constructed by combining pre-treatment, post-treatment, and delta features were 0.901, 0.909, and 0.803, respectively. The AUC, sensitivity, and specificity values of the RSTM constructed using the K-Nearest Neighbor (KNN) classifier on the basis of the combined basic model were 0.944, 0.871, and 0.983, respectively. The Delong test showed that the performance of RSTM was significantly different from that of pre-treatment, post-treatment, and delta models (P < 0.05) but not significantly different from the combined basic model of the three (P > 0.05).ConclusionsThe RSTM constructed using the KNN classifier based on the combined features of before and after neoadjuvant therapy and delta features had the best predictive efficacy for pCR of neoadjuvant therapy. It may emerge as a new clinical tool to assist with individualized management of rectal cancer patients.

Abstract 951: Clinical model for predicting pathological complete response in patients with esophageal and gastroesophageal junction adenocarcinoma after trimodality therapy

Abstract Background: Approximately 25% of patients with esophageal adenocarcinoma achieve pathological complete response (pathCR) after chemoradiotherapy with following surgery (trimodality therapy). The prediction for pathCR would provide an important guidance for decision making regarding surgical strategy. Previous studies have used the logistic regression model to predict pathCR, however, few studies utilized the machine learning models. In this study, we aimed to establish a predictive model for pathCR using the machine learning models using clinical data in patients with GEJ cancer treated by trimodality therapy. Methods: 512 patients with localized esophageal and GEJ adenocarcinoma who received trimodality therapy in MD Anderson Cancer Center between 2002 to 2020 were included. We first performed survival analysis to confirm the survival benefit of pathCR. Then, several prediction models commonly used in machine learning, including logistic regression, LASSO, Random Forest, BART, and xgboost were used to predict pathCR. Each method was trained and validated using 10-fold cross-validation. Results: A total of 125 patients (24 %) achieved a path CR. Patients who achieved path CR had significantly longer overall survival (OS) and relapse free survival (RFS) than &amp;lt;pathCR 387 patients (76%) (median OS, 132 months vs 58 months, respectively, p &amp;lt; 0.002; median RFS, 63 months vs 27 months, respectively, p &amp;lt; 0.001). The logistic regression model indicated non-signet ring cell subtype, T1/T2 stage, negative biopsy after chemoradiation were independent predictive factors for pathCR. Finally, we created machine learning models and LASSO showing the highest predictive ability with AUC value of 0.644 among those models including conventional logistic regression model. The percent partial AUC at lower false positive rate of 0.25 was higher than that of the conventional logistic regression model (% pAUC, 23.7 vs 21.8, respectively) Conclusion: We established the clinical predictive model for pathCR in patients with GEJ cancer treated by trimodality therapy using machine learning method with acceptable predictive ability. Addition of molecular features might further refine the models. Citation Format: Kohei Yamashita, Jacob M. Maronge, Melissa Pool Pizzi, Wayne L. Hofstetter, Aileen Chen, Matheus Sewastjanow-Silva, Ernesto Rosa Vicentini, Meita S. Hirschmann, Anh Ta, Namita Shanbhag, Ying Yuan, Jaffer A. Ajani. Clinical model for predicting pathological complete response in patients with esophageal and gastroesophageal junction adenocarcinoma after trimodality therapy [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 951.

The Significance of a Pathologic Complete Response in Patients With Soft Tissue Sarcoma

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Abstract P6-01-47: Stromal tumor infiltrating lymphocytes and pathological complete response in patients with inflammatory breast cancer treated with neoadjuvant chemotherapy

Abstract Background: Inflammatory breast cancer (IBC) is a rare (1-5%), but aggressive form of breast cancer (BC), accounting for ~10% of BC mortality. In early setting (M0), standard of care is neoadjuvant chemotherapy (NACT), followed by surgery. Nevertheless, outcome is still relatively poor. Pathological complete response (pCR) after NACT is prognostic in BC in general, and can be predicted by a high percentage of stromal tumor infiltrating lymphocytes (sTIL) in the primary tumor. The predictive value of sTIL in IBC has only been sporadically investigated, often in smaller series. Our aim was to determine which variables, including sTIL, are associated with pCR and to determine the prognostic value of pCR in IBC in a large multicentric, retrospective cohort. Patients &amp; Methods: We included patients with IBC treated with NACT+/- anti-Human Epidermal growth factor Receptor 2 (HER2) therapy, followed by surgery from 10/1996 to 10/2021 in 7 different European hospitals. Clinicopathological variables were collected and central pathological review was performed, including sTIL scoring. This study focused on M0 cases. Considered clinicopathological variables were: age, histology, tumor grade, estrogen receptor status (ER), HER2 status, focality (unifocal vs not), and baseline locoregional nodal status (Table 1). Associations between pCR, clinicopathological variables and sTIL were assessed using Firth’s logistic regression models: Model 1 was adjusted for center, Model 2 additionally included all variables of interest. Similarly, linear regression was used to investigate the association between sTIL and clinicopathological features. Univariable and multivariable Cox regression was used to evaluate the role of pCR on disease free survival (DFS), distant recurrence free survival (DRFS) and overall survival (OS). DFS and DRFS were analyzed considering death without the respective event as competing risk. Results: 494 patients were included. The distribution according to receptor status was: ER-/HER2- (24.3%), ER+/HER2- (34.4%), ER+/HER2+ (13%) and ER-/HER2+ (20.2%). pCR rate was 26% and per receptor status: ER-/HER2- (28%), ER+/HER2- (10%), ER+/HER2+ (42%) and ER-/HER2+ (45%). pCR was associated with grade (G3 vs G1/2, OR =2.79 (1.70 − 4.74), p &amp;lt; .001), ER-status (positive vs negative, OR = 0.39 (0.26 − 0.60), p &amp;lt; .001) and HER2 status (positive vs negative, OR = 3.74 (2.43 − 5.81), p &amp;lt; .001) in Model 1. Only the association with HER2 status remained significant in Model 2 (OR = 5.34 (2.83 − 10.47), p &amp;lt; .001). sTIL was scored for 385 patients. Median sTIL was 5.3% [IQR 2.0%;16.7%] and according to receptor status: ER-/HER2- (10%), ER+/HER2- (2.5%), ER+/HER2+ (6.7%) and ER-/HER2+ (8.3%). Higher sTIL was associated with NST (p = .032), grade 3 (p = .015), and ER-negativity (p = .007) in Model 1. This was no longer significant in Model 2, but the direction of the trends was preserved. sTIL was associated with pCR (5% increment, OR = 1.13 (1.05 − 1.22), p = .002), but no longer after adjustment. No association between pCR and sTIL was found stratifying by receptor status. The median FU was 9.4 years and multivariable Cox regression models revealed that ER+ and HER2+ status and achieving pCR were significantly associated with better DFS, DRFS, and OS (Table 1). Conclusion: Our results indicate that patients with HER2+ tumors have a higher probability of achieving pCR and that pCR has an independent prognostic role in IBC. This is the largest IBC study with centrally scored sTIL, demonstrating that sTIL is associated with pCR but its role as an independent predictor of pCR is still not certain. Citation Format: Maxim De Schepper, Ha-Linh Nguyen, François Richard, Florence Lerebours, Roman Vion, Florian Clatot, Anca Berghian, Marion Maetens, Sophia Leduc, Edoardo Isnaldi, Chiara Molinelli, Matteo Lambertini, Frederica Grillo, Gabriele Zoppoli, Luc Dirix, Hilde Wuyts, Kevin Punie, Hans Wildiers, Chantal Remmerie, Ann Smeets, Ines Nevelsteen, Patrick Neven, Anne Salomon, Denis Larsimont, Caroline Duhem, Patrice Viens, François BERTUCCI, Elia Biganzoli, Peter Vermeulen, Giuseppe Floris, Christine Desmedt. Stromal tumor infiltrating lymphocytes and pathological complete response in patients with inflammatory breast cancer treated with neoadjuvant chemotherapy [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-01-47.

Utility of ctDNA in predicting outcome and pathological complete response in patients with bladder cancer as a guide for selective bladder preservation strategies.

563 Background: Muscle-invasive bladder cancer (MIBC) accounts for ~25–30% of all bladder cancer diagnoses. With neoadjuvant chemotherapy (NAC) followed by radical cystectomy (RC) as standard-of-care, the 5-year survival rate ranges from 40%–60%. Bladder-sparing protocols (BSP) have emerged as a feasible alternative to RC for MIBC treatment, however better tools are needed. In this study, we evaluated the prognostic value of circulating tumor DNA (ctDNA) in predicting recurrence in patients who achieved pathological complete response (pCR). Methods: We analyzed a previously described cohort of 68 patients (656 plasma samples; Christensen et al., JCO 2019) with MIBC who received NAC prior to cystectomy. Patients had an updated median follow-up of 58.94 months (range: 7.19-81.77) post-cystectomy. ctDNA was analyzed at baseline (before NAC; N=64), and prior to cystectomy (N=65) using a commercially available assay (Signatera, Natera, Inc.). Additionally, exploratory RNA-Seq was performed on tumors from 59 patients (samples with &gt;5M total counts were utilized). Pathway analysis was used to compare ctDNA-positive and ctDNA-negative patients who failed to achieve pCR. Results: Of the 64 patients with ctDNA results available at baseline, 59.4% (38/64) tested ctDNA-negative, and of these 84.2% (32/38) achieved pCR. Furthermore, 40.6% (26/64) tested ctDNA-positive, and only 34.6% (9/26) achieved pCR. Likewise, prior to cystectomy, 83.9% (52/62) of patients were ctDNA-negative, and 80.7% (42/52) achieved pCR, while none of the ctDNA-positive patients achieved pCR (positive predictive value 100%; negative predictive value 80.8%). Based on both ctDNA timepoints, the probability of ctDNA-negative patients to achieve pCR was significantly higher than ctDNA-positive patients ( p&lt;0.0001). Notably, ctDNA-positive patients without pCR demonstrated significantly poorer RFS and OS compared to the ctDNA-negative patients, at both timepoints (Baseline: RFS; HR=8.2, p=0.017, OS; HR=8.4, p=0.015, prior to cystectomy: RFS; HR=5.2, p=0.0078, OS; HR=4.8, p=0.012). Furthermore, ctDNA status at baseline and before cystectomy was a better predictor of RFS compared to pCR (HR=8.5, p&lt;0.0001, HR=14, p&lt;0.0001, respectively). Transcriptomic pathway analysis of patients who did not achieve pCR showed an enrichment of oncogenic pathways, namely EMT and angiogenesis, in tumors from ctDNA positive patients, whereas tumors from ctDNA negative patients showed an enrichment of anti-tumor immune signatures, including IFNα and IFNγ. Conclusions: Absence of ctDNA was significantly associated with pCR both at baseline and prior to cystectomy, identifying patients who may benefit from BSP. Larger cohorts are warranted to test the prognostic value of ctDNA combined with transcriptomic profiling in informing patient selection for avoiding cystectomy.