Prediction Of Pathological Complete Response Research Articles

Abstract PO3-27-01: Prediction of pathological complete response to neoadjuvant treatment in loco-regional advancedbreast cancer by using advanced PET/CT metabolic parameters

Abstract Prediction of pathological complete response to neoadjuvant treatment in loco-regional advanced breast cancer by using advanced PET/CT metabolic parameters. Abstract: Background: The standard treatment for locally advanced breast cancer (LABC) is neoadjuvant systemic therapy followed by surgery. The usefulness of FDG PET/CT for evaluating the treatment efficacy in breast cancer is well established; however, the role of quantitative functional PET/CT parameters, such as the metabolic tumor volume (MTV) and total lesion glycolysis (TLG), remains inconclusive. The aim of this study was to determine the correlation between quantitative functional parameters derived from PET/CT in LABC patients at staging and the response to neoadjuvant therapy. Methods: Seventy-eight consecutive patients (aged 25-82 y) with confirmed LABC (stages II-III) who underwent PET/CT as a part of their staging assessment and received neoadjuvant therapy followed by surgery were retrospectively included in this study. All patients underwent an FDG PET/CT at staging; and values of SUV max, SUV mean, MTV, and TLG were determined from the PET/CT studies. The patients were divided into two groups, based on the completeness of histopathologic response to neoadjuvant therapy, both in the primary tumor and axillary lymph nodes. Clinical, imaging and demographic data were collected from patient’s medical record. Results: A complete pathologic response (pCR) was achieved in 33/78 (42%) patients. Among patients with ER/PR positive, Her2-neu negative tumors, in 23/27 (85%) patients only partial pathologic response was reached; 91% of patients with ER/PR negative, Her2-neu positive and 43% of Triple-negative patients had complete pathological? response. For the primary breast tumor response, a higher SUV max, 10.7±5.34 for pCR vs. 7.24±4.62 for non-pCR group was measured, p-value=0.003; threshold value of 6.625. A higher SUV mean in the primary tumor resulted in a significantly higher rate of pCR, 5.95±2.36 vs 4,4±2.16, p=0.004, with a threshold value of 5.795. Neither MTV, not TLG were predictive parameters of the primary tumor response. For lymph nodes response, SUV max and SUV mean had no role in the prediction of pCR. Statistically significant difference however, was found between pCR and non-pCR in MTV values in the involved lymph nodes, with a median =2.13 cm³ [0-7.85] in pCR group vs 5.43 cm³ [3.11-15.3] in non-pCR, p=0.01 (threshold value 2.375) as well as in TLG measurements, with a median of 4.64 [0-19.8] vs. 12.7 [3.21-23.1], p=0.047, (25-75 IQR). Conclusion: Primary breast tumor SUV max/mean values have a significant correlation with the treatment response and threshold values were found. Low MTV and TLG measurements in lymph nodes may predict pCR. Neither MTV nor TLG had were found to have a significant role in the prediction of the primary tumor response. Citation Format: Maisun Shamya, Olga Kagna, Zohar Keidar, Tala Palchan-Hazan, Salem Billan, Roxolyana Abdah-Bortnyak. Prediction of pathological complete response to neoadjuvant treatment in loco-regional advancedbreast cancer by using advanced PET/CT metabolic parameters [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 PO3-27-01.

Abstract PO1-15-12: Circulating tumor DNA (ctDNA) for prediction of pathologic complete response (pCR) after 12 weeks of pembrolizumab + trastuzumab + pertuzumab in HER2-enriched early breast cancer: WSG-Keyriched-1 trial

Abstract Background ctDNA testing is emerging as an important biomarker in early breast cancer (eBC). However, its value in prediction of tumor response to de-escalated, chemotherapy-free neoadjuvant therapy (NAT) remains underexplored. In WSG-Keyriched-1 (NCT03988036), a single-arm phase 2 trial, we investigated for the first time a chemotherapy-free NAT with dual HER2 blockade and pembrolizumab in HER2- enriched HER2+ eBC. In this pre-specified translational analysis, we evaluated whether ctDNA measurement could predict pCR. Methods 48 patients (pts) with HER2 2+ (ISH+) or 3+ eBC (stage I-III) and HER2-enriched subtype by PAM50 were included. Pts received pembrolizumab (200 mg), trastuzumab biosimilar (loading dose (LD) 8 mg/kg bodyweight (BW), maintenance dose (MD) 6 mg/kg BW), and pertuzumab (LD 840 mg/kg BW, MD 420 mg/kg BW) q3w for 12 weeks. Primary objective was pCR (ypT0/is ypN0). ctDNA was analyzed in 92 plasma samples collected from 31 pts at baseline (BL) and week 3 of NAT and 30 pts at end of treatment (EOT). Sequencing libraries with unique molecular identifiers were constructed from cell-free DNA and hybridization panels with ≤50 patient-specific somatic mutations from tumor sequencing were used for enrichment. Libraries were sequenced to an ultra-high depth of 100,000×. Sequencing data was analyzed using a combination of public pipelines (megSAP and umiVar2 for mapping and deduplication) and custom in-house scripts (to extract the allele counts for patient-specific variants). To reduce the error rate, only corrected reads with ≥4 duplicates were used. p-value for ctDNA detection was calculated using the total variant count, depth and sample-specific error. Association between ctDNA with pCR and other clinical parameters were assessed with Chi-square or Fisher’s exact test and univariable logistic regression. Additionally, bivariable logistic regressions for pCR were performed with ctDNA and either cT, cN, or grade. Results ctDNA was detected (ctDNA+) in 58.0% (n=18/31) of pts at BL, 9.7% (n=3/31) at week 3, and 10% (n=3/30) at EOT. ctDNA was cleared by week 3 in 83.3% of pts (n=15/18). Compared with ctDNA-negative cases (ctDNA-) at BL, those ctDNA+ more often had cT2-3 disease (94.4%, n=17/18, vs 38.5%, n=5/13; p=.001) and lymph node involvement (61.1%, n=11/18, vs 0%, n=0/13; p< .001). 72.2% (n=13/18) of ctDNA+ and 61.5% (n=8/13) of ctDNA- cases at BL were grade 3 (p=.53). Each of the 3 pts who remained ctDNA+ at week 3 was node-positive; all pts remaining ctDNA- at week 3 were node-negative. pCR rate was 38.9% (n=7/18) in ctDNA+ vs 76.9% (n=10/13) in ctDNA- pts at BL (p=.067), 0% (n=0/3) in ctDNA+ vs 60.7% (n=17/28) in ctDNA- at week 3 (p=.081), and 33.3% (n=1/3) in ctDNA+ vs 59.3% (n=16/27) in ctDNA- at EOT (p=.565). pCR rate was highest in pts who remained ctDNA- throughout week 3 (76.9%, n=10/13), compared to pts with ctDNA cleared by week 3 (46.7%, n=7/15), and pts remaining ctDNA+ (0%, n=0/3, p=.024). ctDNA at BL was predictive for pCR in univariable analysis (odds ratio, OR 0.22, 95%CI 0.04- 0.93, area under curve, AUC=0.69). Bivariable models including ctDNA and grade yielded the highest accuracy for analyses with ctDNA at BL (AUC=0.77), week 3 (AUC=0.79), and with ctDNA change from BL to week 3 (AUC=0.87). Conclusions Absence of ctDNA prior to and during NAT, as well as early clearance of ctDNA associates with a higher pCR rate in eBC pts with HER2-enriched intrinsic subtype treated with chemotherapy-free pembrolizumab plus dual HER2 blockade. These results are in line with prior data with more intensive therapy from I-SPY 2 and NeoALTTO trials in HER2+ eBC and indicate that ctDNA analysis during NAT appears feasible for real- time monitoring of response to treatment and could be used to guide escalation/de-escalation strategies. Further trials with a larger number of pts and pre-designed follow-up are needed to confirm the role of ctDNA for prediction of tumor response and relapse. Citation Format: Monika Graeser, Sherko Kuemmel, Oleg Gluz, Christopher Schroeder, Leon Schuetz, Olga Kelemen, Stephan Ossowski, Katarzyna Jozwiak, Mattea Reinisch, Athina Kostara, Iris Scheffen, Kerstin Lüdtke-Heckenkamp, Felix Hilpert, Angela Kentsch, Carsten Ziske, Reinhard Depenbusch, Michael Braun, Jens-Uwe Blohmer, Matthias Christgen, Stephan Bartels, Hans-Heinrich Kreipe, Enrico Pelz, Ulrike Nitz, Peter Schmid, Nadia Harbeck, Andreas Hartkopf. Circulating tumor DNA (ctDNA) for prediction of pathologic complete response (pCR) after 12 weeks of pembrolizumab + trastuzumab + pertuzumab in HER2-enriched early breast cancer: WSG-Keyriched-1 trial [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 PO1-15-12.

Abstract PO1-15-05: Gene expression signature as a predictor of pathological complete response to neoadjuvant docetaxel, carboplatin, trastuzumab, and pertuzumab treatment in locally advanced HER2-positive breast cancer

Abstract Background: Neoadjuvant pertuzumab and trastuzumab in combination with chemotherapy can achieve pathological complete response (pCR, defined as ypT0/is ypN0) in approximately 40–60% of patients with human epidermal growth factor receptor 2-positive (HER2+) breast cancer (BC). Patients who achieve pCR have significantly improved survival. Therefore, the prediction of pCR is crucial for optimizing neoadjuvant therapy. Methods: To identify a gene expression signature that could predict pCR in patients with HER2+ BC, we used the nCounter Breast Cancer 360TM V2 panel to quantify the expression of 758 genes in 104 HER2+ BC samples from patients who received neoadjuvant docetaxel, carboplatin, trastuzumab, and pertuzumab (TCHP) chemotherapy and subsequent curative breast surgery. Data normalization, differentially expressed gene (DEG) analysis, and gene set enrichment analysis (GSEA) were performed using the NanoTube Bioconductor package. Using the elastic net logistic regression model with optimal hyperparameters determined by 5-fold cross-validation (5-CV), we developed a chemosensitivity score based on the expression of significant DEGs (genes with absolute log2-fold-change ≥0.5 and q-value < 0.1). We tested its predictive value for pCR using multivariable logistic regression analysis. Results: The median age at diagnosis was 49.5 years. The clinical stage was II in 53 (51%) patients and III in 51 (49%) patients at diagnosis. Ninety (86.5%) patients had a HER2 immunohistochemistry (IHC) score of 3+, while the remaining 14 (13.5%) patients had HER2 IHC 2+ but HER2 amplification detected by in situ hybridization. Pathological evaluation of surgical specimens revealed that 55 (52.9%) patients achieved pCR. Menopausal status (p = 0.01), PAM50 intrinsic subtype (p = 0.002), hormone receptor (HR) status (p < 0.001), and HER2 IHC score (p = 0.019) were significantly correlated with the pCR rate. DEG analysis using the PAM50 intrinsic subtype, HR status, and HER2 IHC score as covariates revealed 6 downregulated DEGs and 33 upregulated DEGs in patients who achieved pCR compared to patients who did not (Table 1). The 39 DEGs were significantly enriched in nine preselected MSigDB hallmark gene sets representing immunological processes (p < 0.001). The optimal elastic net logistic regression model containing 39 DEGs achieved the mean 5-CV area under the receiver operating characteristic curve of 0.81. The chemosensitivity score calculated using the optimal model parameters was significantly correlated with pCR after adjustment for the PAM50 intrinsic subtype, HR status, and HER2 IHC score (p < 0.001). GSEA revealed eight hallmark gene sets that were significantly up- or downregulated in samples with pCR, including epithelial-mesenchymal transition (normalized enrichment score [NES] = -2.16; q < 0.001), late estrogen response (NES = -2.01; q = 0.004), early estrogen response (NES = -1.972; q = 0.005), and inflammatory response (NES = 1.53; q = 0.06). Conclusion: Our findings demonstrate that gene expression patterns can predict the response to neoadjuvant TCHP chemotherapy in HER2+ BC. The functional characterization of 39 DEGs suggests that the tumor microenvironment, including adjacent immune cells, plays a significant role in modulating the response to neoadjuvant chemotherapy in HER2+ BC. Table 1. Significant DEGs Citation Format: Junghoon Shin, Ji-Yeon Kim, Eun Yoon Cho, Seok Won Kim, Jeong Eon Lee, Hyunwoo Lee, Yeon Hee Park, Jin Seok Ahn, Young-Hyuck Im. Gene expression signature as a predictor of pathological complete response to neoadjuvant docetaxel, carboplatin, trastuzumab, and pertuzumab treatment in locally advanced HER2-positive breast cancer [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 PO1-15-05.

Abstract PO2-17-02: Can body mass index be a complete pathological response predictor in breast cancer patients submitted to neoadjuvant chemotherapy? A real-world data trial

Abstract Background: Breast cancer is a prevalent disease, and Its incidence has increased annually worldwide. The prognosis depends on several factors, such as patient characteristics, tumor characteristics and treatment efficacy. In this scenario, neoadjuvant chemotherapy (NAC), previously used only to make surgical approach possible in advanced cases, has been increasingly used in more aggressive initial tumors such as HER 2 positive and triple negative and, in these tumors, the presence of pathological complete response is directly correlated with survival and the disease-free interval. Obesity is very common in Brazil and in the world, and its incidence grows annually. There is a direct relationship between the risk of breast cancer and obesity, especially in postmenopausal women. Justification: Many authors suggest that obese patients are less likely to achieve this pathological complete response (pCR). Real-world data is important to evaluate how patients behave in the real world, only 5% of the patients are enrolled in clinical trials. Most of the clinical trials have strict inclusion and exclusion criteria that ends up selecting a patient that almost doesn't exist in the real world. Objective: To correlate complete pathological response with Body Mass Index (BMI) and, secondarily, to evaluate the relationship between overall and disease-free survival with BMI. Methods: This was a retrospective observational study that has evaluated patients with invasive breast cancer in two Brazilian hospitals. Clinical and pathological characteristics were extracted from the database, being eligible the patients diagnosed with invasive breast cancer, stages I to III, and who underwent neoadjuvant chemotherapy (NAC). Patients were categorized using the World Health Organization (WHO) definition: BMI 18.5 to < 25 kg/m2: normal weight; BMI 25 to < 30 kg/m2: overweight; BMI ≥30kg/m2: obesity. pCR was defined as the absence of breast and axillary invasive tumor. The t test or chi-square test were used to individually analyze the association of each variable between the groups with and without RPc. Univariate and multivariate analyzes to calculate odds ratios (OR) and 95% confidence intervals (CI) of the independent variables BMI, age, clinical stage, histological type, and subtype of cancer, correlated with pCR. where p value < 0.05 was considered statistically significant. Results: 1,779 records were included in the analyses, with a mean age of 50 years at diagnosis and a mean BMI of 28.08 kg/m2. Most patients had stage III cancer (68.1%) at diagnosis and the invasive carcinoma nonspecial type was predominant (95.2%). Immunohistochemistry allowed us to divide the groups into Luminal A like (12.4%), Luminal B like (31.1%), HER2+ like (25.3%) and triple negative like (31.2%). After NAC, 1461 had residual disease and 390 (22.3%) had a pCR. Regarding stages, 2% of the patients were in stage I, 30% in stage II and 68% in stage III. No correlation was found between BMI categories and pCR (p=0.7607). In the multivariate analysis, the results were also not significant for the correlation between the BMI categories after adjusting for age, clinical stage, tumor histology and subtype of breast cancer. We observed, however, that the most aggressive types of cancer were predictors of pathologic complete response. The estimated average overall survival (OS) was 102 months for those of normal weight, 99 months for the overweight ones and 101 months for the obese ones. As for recurrence-free survival, the data were immature. Conclusion: This study with Brazilian real-world data found no correlation between BMI and pCR after NAC and BMI and OS in patients with breast cancer. As an additional finding, most patients were obese or overweight at diagnosis (69%) and public policies should be encouraged to act in this scenario. Trials considering stratification of patients between different regions of the country or specific subtype of cancer can elucidate this finding. Citation Format: Fernanda Grace Bauk Richter, Andre Mattar, Marcelo Antonini, Juliana Real, Luis Henrique Gebrim, Reginaldo Coelho Lopes. Can body mass index be a complete pathological response predictor in breast cancer patients submitted to neoadjuvant chemotherapy? A real-world data trial [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 PO2-17-02.

Image-based artificial intelligence for the prediction of pathological complete response to neoadjuvant chemoradiotherapy in patients with rectal cancer: a systematic review and meta-analysis.

Artificial intelligence (AI) holds enormous potential for noninvasively identifying patients with rectal cancer who could achieve pathological complete response (pCR) following neoadjuvant chemoradiotherapy (nCRT). We aimed to conduct a meta-analysis to summarize the diagnostic performance of image-based AI models for predicting pCR to nCRT in patients with rectal cancer. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A literature search of PubMed, Embase, Cochrane Library, and Web of Science was performed from inception to July 29, 2023. Studies that developed or utilized AI models for predicting pCR to nCRT in rectal cancer from medical images were included. The Quality Assessment of Diagnostic Accuracy Studies-AI was used to appraise the methodological quality of the studies. The bivariate random-effects model was used to summarize the individual sensitivities, specificities, and areas-under-the-curve (AUCs). Subgroup and meta-regression analyses were conducted to identify potential sources of heterogeneity. Protocol for this study was registered with PROSPERO (CRD42022382374). Thirty-four studies (9933 patients) were identified. Pooled estimates of sensitivity, specificity, and AUC of AI models for pCR prediction were 82% (95% CI: 76-87%), 84% (95% CI: 79-88%), and 90% (95% CI: 87-92%), respectively. Higher specificity was seen for the Asian population, low risk of bias, and deep-learning, compared with the non-Asian population, high risk of bias, and radiomics (all P < 0.05). Single-center had a higher sensitivity than multi-center (P = 0.001). The retrospective design had lower sensitivity (P = 0.012) but higher specificity (P < 0.001) than the prospective design. MRI showed higher sensitivity (P = 0.001) but lower specificity (P = 0.044) than non-MRI. The sensitivity and specificity of internal validation were higher than those of external validation (both P = 0.005). Image-based AI models exhibited favorable performance for predicting pCR to nCRT in rectal cancer. However, further clinical trials are warranted to verify the findings.

Prediction of pathological complete response in locally advanced head and neck squamous cell carcinoma treated with neoadjuvant chemo-immunotherapy using volumetric multisequence MRI histogram analysis.

This study aimed to develop a multisequence MRI-based volumetric histogram metrics model for predicting pathological complete response (pCR) in advanced head and neck squamous cell carcinoma (HNSCC) patients undergoing neoadjuvant chemo-immunotherapy (NCIT) and compare its predictive performance with AJCC staging and RECIST 1.1 criteria. Twenty-four patients with locally advanced HNSCC from a prospective phase II trial were enrolled for analysis. All patients underwent pre- and post-NCIT MRI examinations from which whole-tumor histogram features were extracted, including T1WI, T2WI, enhanced T1WI (T1Gd), diffusion-weighted imaging (DWI) sequences, and their corresponding apparent diffusion coefficient (ADC) maps. The pathological results divided the patients into pathological complete response (pCR) and non-pCR (N-pCR) groups. Delta features were calculated as the percentage change in histogram features from pre- to post-treatment. After data reduction and feature selection, logistic regression was used to build prediction models. ROC analysis was performed to assess the diagnostic performance. Eleven of 24 patients achieved pCR. Pre_T2_original_firstorder_Minimum, Post_ADC_original_firstorder_MeanAbsoluteDeviation, and Delta_T1Gd_original_firstorder_Skewness were associated with achieving pCR after NCIT. The Combined_Model demonstrated the best predictive performance (AUC 0.95), outperforming AJCC staging (AUC 0.52) and RECIST 1.1 (AUC 0.72). The Pre_Model (AUC 0.83) or Post-Model (AUC 0.83) had a better predictive ability than AJCC staging. Multisequence MRI-based volumetric histogram analysis can non-invasively predict the pCR status of HNSCC patients undergoing NCIT. The use of histogram features extracted from pre- and post-treatment MRI exhibits promising predictive performance and offers a novel quantitative assessment method for evaluating pCR in HNSCC patients receiving NCIT.

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

BackgroundSo far, baseline Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has played a key role for the application of sophisticated artificial intelligence-based models using Convolutional Neural Networks (CNNs) to extract quantitative imaging information as earlier indicators of pathological Complete Response (pCR) achievement in breast cancer patients treated with neoadjuvant chemotherapy (NAC). However, these models did not exploit the DCE-MRI exams in their full geometry as 3D volume but analysed only few individual slices independently, thus neglecting the depth information. MethodThis study aimed to develop an explainable 3D CNN, which fulfilled the task of pCR prediction before the beginning of NAC, by leveraging the 3D information of post-contrast baseline breast DCE-MRI exams. Specifically, for each patient, the network took in input a 3D sequence containing the tumor region, which was previously automatically identified along the DCE-MRI exam. A visual explanation of the decision-making process of the network was also provided. ResultsTo the best of our knowledge, our proposal is competitive than other models in the field, which made use of imaging data alone, reaching a median AUC value of 81.8%, 95%CI [75.3%; 88.3%], a median accuracy value of 78.7%, 95%CI [74.8%; 82.5%], a median sensitivity value of 69.8%, 95%CI [59.6%; 79.9%] and a median specificity value of 83.3%, 95%CI [82.6%; 84.0%], respectively. The median and CIs were computed according to a 10-fold cross-validation scheme for 5 rounds. ConclusionFinally, this proposal holds high potential to support clinicians on non-invasively early pursuing or changing patient-centric NAC pathways.

Prediction of pathologic complete response to neoadjuvant chemoradiation in locally advanced rectal cancer.

To investigate the predictive factors of pathologic complete response (pCR) in locally advanced rectal cancer (LARC) patients who had been treated with neoadjuvant chemoradiation (nCRT). For this retrospective study, 53 LARC patients (37 males and 16 females; age range 25 to 79 years) were selected. Clinical characteristics, baseline mrTNM staging, MR gross tumor volumes (GTV), and pCR were evaluated. The diagnostic accuracy of GTV for predicting pCR was calculated. Among 53 LARC patients, 15 patients achieved pCR (28.3%), while 38 patients achieved non-pCR. Only three (5.7%) out of 53 patients did not downstage after nCRT. GTV and tumor differentiation were the significant prognostic parameters for predicting pCR. A tumor volume threshold of 21.1 cm3 was determined as a predictor for pCR, with a sensitivity of 84% and specificity of 47%. In addition, GTV was associated with mrN stage, circumferential resection margin (CRM) status, extramural vascular invasion (EMVI) status, and pretreatment serum CEA level. Tumor volume and tumor differentiation have significant predictive values in preoperative assessment of pCR among LARC patients. These findings aid clinicians to discriminate those patients who may likely benefit from preoperative regimens and to make optimal treatment plans.

Artificial intelligence measured 3D body composition to predict pathological response in rectal cancer patients.

The treatment of locally advanced rectal cancer (LARC) is moving towards total neoadjuvant therapy and potential organ preservation. Of particular interest are predictors of pathological complete response (pCR) that can guide personalized treatment. There are currently no clinical biomarkers which can accurately predict neoadjuvant therapy (NAT) response but body composition (BC) measures present as an emerging contender. The primary aim of the study was to determine if artificial intelligence (AI) derived body composition variables can predict pCR in patients with LARC. LARC patients who underwent NAT followed by surgery from 2012 to 2023 were identified from the Australian Comprehensive Cancer Outcomes and Research Database registry (ACCORD). A validated in-house pre-trained 3D AI model was used to measure body composition via computed tomography images of the entire Lumbar-3 vertebral level to produce a volumetric measurement of visceral fat (VF), subcutaneous fat (SCF) and skeletal muscle (SM). Multivariate analysis between patient body composition and histological outcomes was performed. Of 214 LARC patients treated with NAT, 22.4% of patients achieved pCR. SM volume (P = 0.015) and age (P = 0.03) were positively associated with pCR in both male and female patients. SCF volume was associated with decreased likelihood of pCR (P = 0.059). This is the first study in the literature utilizing AI-measured 3D Body composition in LARC patients to assess their impact on pathological response. SM volume and age were positive predictors of pCR disease in both male and female patients following NAT for LARC. Future studies investigating the impact of body composition on clinical outcomes and patients on other neoadjuvant regimens such as TNT are potential avenues for further research.

Long-Term Outcomes of S-1 Combined With Low-Dose Docetaxel as Neoadjuvant Chemotherapy (N-1 Study, Phase II Trial) in Patients With Operable Breast Cancer

BackgroundWe previously reported that S-1 and low-dose docetaxel (DOC) (N-1 study, phase II trial) could be a well-tolerated and effective neoadjuvant chemotherapies (NACs) for patients with operable breast cancer. Herein, we analyzed the long-term outcomes and developed clinicopathological and molecular predictors of pathological complete response (pCR). Patients and MethodsEighty-three patients received S-1 (40 mg/m2 orally on days 1-14) and DOC (40 mg/m2 intravenously on day 1) every 3 weeks for 4 to 8 cycles. Disease-free survival (DFS) and overall survival (OS) were analyzed for each population with a pCR status. To assess the relationship between pCR and clinicopathological factors such as tumor-infiltrating lymphocytes (TILs, 1+ <10%, 2+ 10%-50%, and 3+ >50%) and nuclear grade (NG), microarray was used to compare the microRNA profiles of the pCR and non-pCR groups using core needle biopsy specimens. ResultsWith a median follow-up duration of 99.0 (range, 9.0-129.0) months, the 5-year DFS and OS rates were 80.7% and 90.9%, respectively. The 5-year OS rate of the pCR group was significantly better than that of the non-pCR group (100% vs. 86.2%, p = .0176). Specifically, in triple-negative patients, the difference was significant (100% vs. 60.0%, p = .0224). Multivariate analysis revealed that high TILs (≥2–3+) and NG 2–3 independently predicted pCR. Microarray data revealed that 3 miRNAs (miR-215-5p, miR-196a-5p, and miR-196b-5p) were significantly upregulated in the pCR group. ConclusionOur NAC regimen achieved favorable long-term outcomes and significantly improved OS in the pCR group. High TILs, NG 2–3, and some miRNAs may be predictors of pCR.

Abstract A066: Machine learning elucidates biology of response within and outside the mechanisms of action of therapeutic agents in the I-SPY2 breast cancer TRIAL

Abstract Background: Machine learning (ML) in translational medicine has led to prediction of clinical outcomes and identification of new biomarkers. We employ ML in prediction of pathologic complete response (pCR) in high-risk breast cancer patients in the neoadjuvant I-SPY2 TRIAL where not all novel agents have strong predictive biomarkers. Leveraging a ML approach using progressively expanded candidate genes, we explore the limitations of using only known mechanisms of action in predicting pCR, and the extent to which biology outside known drug action improves response prediction in the first 10 arms of the trial. Methods: ML random forest models were developed in I-SPY2 patients (n=982) with pre-treatment gene expression and pCR data across 10 treatment arms (PMID: 35623341), including inhibitors of HER2: neratinib (N), pertuzumab (P), TDM1/P; AKT (MK-2206); IGF1R (ganitumab); HSP90 (ganetespib); PARP/DNA repair (veliparib/carboplatin, VC); ANG1/2 (trebananib, T); immune checkpoints (PD1-inh); and Control (Ctr). Each HR/HER2 receptor/treatment arm subset (m=27) was evaluated independently. We employed a three-pronged feature-selection approach using (1) genes restricted to known mechanism of action of individual I-SPY2 agents (k=10 to 88 genes); (2) genes expanded to include targeted pathways for all 10 agents/combinations (k=282); and (3) an unbiased whole genome approach (k=17,990). Samples were partitioned with 75% used for training and cross-validation, and 25% held out as test sets. Predictive ML models were defined as those with performance ≥ 0.90 based on different performance metrics (e.g., AUC, sensitivity, specificity). Results: For each of the 27 subtype-treatment subsets, at least one high performing model was identified. In 6 subtype-treatment subsets, mechanism of action genes were sufficient to predict pCR: AKT/PI3K/HER genes in HR+HER2- N and HR-HER2+ P; DNA repair genes in HR+HER2- VC; angiogenesis-associated genes in HR+HER2+ T; and immune-associated genes in both HR+HER2- and HR-HER2- PD1-inh subsets. Expanded targeted pathway models were required to identify predictive models in 8 additional subtype-treatment pairs from the N, T-DM1/P, MK-2206, VC, T, and HER2+ Ctr arms, with significant contribution of DNA repair, immune, and HSP90 genes for multiple arms. A genome-wide approach was required for the remaining 13 subtype-treatment pairs with no previous models from the N, P, MK-2206, ganitumab, ganetespib, T, and HER2- Ctr arms. Even for subtype-treatment pairs where mechanism of action gene sets was sufficient for reasonable models, expanded gene sets resulted in improved performance. For instance, metabolism genes improved model performance for HR-HER2+ in N and Ctr, and for HR+HER2- in the PD1-inh arm; and mitochondrial and protein folding dysfunction genes improved response prediction in HR-HER2- in the ganetespib arm. Conclusion: Our study identifies mechanism of action biomarkers associated with response to each drug and elucidates possible off-target effects contributing to observed drug sensitivity and resistance. Citation Format: Rosalyn W. Sayaman, Denise M. Wolf, Christina Yau, Julia Wulfkhule, Emanuel F. Petricoin, Lamorna Brown-Swigart, Tam Binh Bui, Gillian L. Hirst, Diane Heditsian, W. Fraser Symmans, Angela DeMichele, Mark LaBarge, Laura J. Esserman, Laura van ‘t Veer. Machine learning elucidates biology of response within and outside the mechanisms of action of therapeutic agents in the I-SPY2 breast cancer TRIAL [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr A066.

CD10 expression as a potential predictor of pathological complete response in ER-negative and triple-negative breast cancer patients treated with anthracycline-based neoadjuvant chemotherapy

BackgroundNeoadjuvant chemotherapy (NCT) can induce a pathological complete response (pCR) in breast cancer patients, leading to improved outcomes. However, predicting which patients will achieve pCR remains a challenge. CD10, a myoepithelial marker, has shown diagnostic and prognostic value in metastatic tumors. Its potential as a predictor of chemosensitivity to anthracycline-based NCT in breast cancer is unknown. AimThis retrospective study aimed to investigate the potential of CD10 cancer cell expression as a predictive marker of chemosensitivity in breast cancers treated with anthracycline-based neoadjuvant chemotherapy. MethodsWe analyzed 130 patients with invasive ductal carcinoma who received anthracycline-based NCT. CD10 expression was assessed by immunohistochemistry on pre-treatment biopsies. Statistical analysis evaluated the association between CD10 expression and pCR rates. ResultsUnivariate analysis revealed that ER-positive and CD10-negative tumors had lower pCR rates [OR 7.4830 (95% CI 2.7762–20.1699); p = 0.0001]. Multivariate analysis confirmed ER status as a strong predictor of poor response [OR 0.085 (95% CI 0.024–0.30); p < 0.001] and CD10 expression as a predictor of a favourable response [OR 0.11 (0.8–0.19); p = 0.049]. CD10 expression significantly predicted pCR in ER-negative cases [OR 0.1098 (0.0268–0.4503); p = 0.0022] and triple-negative breast cancer [OR 0.0966 (95% CI 0.0270–0.3462); p = 0.0003]. Concordance was observed between core biopsies and excised samples. ConclusionPositive CD10 cancer cell expression may predict increased response to anthracycline-based neoadjuvant chemotherapy in ER-negative and triple-negative breast cancer cases. Further research is needed to validate these findings in larger cohorts and determine the clinical utility of CD10 as a predictive marker.

Machine learning prediction of pathological complete response and overall survival of breast cancer patients in an underserved inner-city population

BackgroundGeneralizability of predictive models for pathological complete response (pCR) and overall survival (OS) in breast cancer patients requires diverse datasets. This study employed four machine learning models to predict pCR and OS up to 7.5 years using data from a diverse and underserved inner-city population.MethodsDemographics, staging, tumor subtypes, income, insurance status, and data from radiology reports were obtained from 475 breast cancer patients on neoadjuvant chemotherapy in an inner-city health system (01/01/2012 to 12/31/2021). Logistic regression, Neural Network, Random Forest, and Gradient Boosted Regression models were used to predict outcomes (pCR and OS) with fivefold cross validation.ResultspCR was not associated with age, race, ethnicity, tumor staging, Nottingham grade, income, and insurance status (p > 0.05). ER−/HER2+ showed the highest pCR rate, followed by triple negative, ER+/HER2+, and ER+/HER2− (all p < 0.05), tumor size (p < 0.003) and background parenchymal enhancement (BPE) (p < 0.01). Machine learning models ranked ER+/HER2−, ER−/HER2+, tumor size, and BPE as top predictors of pCR (AUC = 0.74–0.76). OS was associated with race, pCR status, tumor subtype, and insurance status (p < 0.05), but not ethnicity and incomes (p > 0.05). Machine learning models ranked tumor stage, pCR, nodal stage, and triple-negative subtype as top predictors of OS (AUC = 0.83–0.85). When grouping race and ethnicity by tumor subtypes, neither OS nor pCR were different due to race and ethnicity for each tumor subtype (p > 0.05).ConclusionTumor subtypes and imaging characteristics were top predictors of pCR in our inner-city population. Insurance status, race, tumor subtypes and pCR were associated with OS. Machine learning models accurately predicted pCR and OS.

Radiomics based on 18F-FDG PET/CT for prediction of pathological complete response to neoadjuvant therapy in non-small cell lung cancer.

This study aimed to establish and evaluate the value of integrated models involving 18F-FDG PET/CT-based radiomics and clinicopathological information in the prediction of pathological complete response (pCR) to neoadjuvant therapy (NAT) for non-small cell lung cancer (NSCLC). A total of 106 eligible NSCLC patients were included in the study. After volume of interest (VOI) segmentation, 2,016 PET-based and 2,016 CT-based radiomic features were extracted. To select an optimal machine learning model, a total of 25 models were constructed based on five sets of machine learning classifiers combined with five sets of predictive feature resources, including PET-based alone radiomics, CT-based alone radiomics, PET/CT-based radiomics, clinicopathological features, and PET/CT-based radiomics integrated with clinicopathological features. Area under the curves (AUCs) of receiver operator characteristic (ROC) curves were used as the main outcome to assess the model performance. The hybrid PET/CT-derived radiomic model outperformed PET-alone and CT-alone radiomic models in the prediction of pCR to NAT. Moreover, addition of clinicopathological information further enhanced the predictive performance of PET/CT-derived radiomic model. Ultimately, the support vector machine (SVM)-based PET/CT radiomics combined clinicopathological information presented an optimal predictive efficacy with an AUC of 0.925 (95% CI 0.869-0.981) in the training cohort and an AUC of 0.863 (95% CI 0.740-0.985) in the test cohort. The developed nomogram involving radiomics and pathological type was suggested as a convenient tool to enable clinical application. The 18F-FDG PET/CT-based SVM radiomics integrated with clinicopathological information was an optimal model to non-invasively predict pCR to NAC for NSCLC.

Tumor size, HER-2 status, CA125, CEA, SII, and PNI: key predictors of pathological complete response in LABC patients

Tumor size, HER-2 status, CA125, CEA, SII, and PNI: key predictors of pathological complete response in LABC patients

Multiparametric MRI-based radiomics combined with pathomics features for prediction of the efficacy of neoadjuvant chemotherapy in breast cancer

PurposeThe aim of this study is to investigate a new method that combines radiological and pathological breast cancer information to predict discrepancies in pathological responses for individualized treatment planning. We used baseline multiparametric magnetic resonance imaging and hematoxylin and eosin-stained biopsy slides to extract quantitative feature information and predict the pathological response to neoadjuvant chemotherapy in breast cancer patients. MethodsWe retrospectively collected data from breast cancer patients who received neoadjuvant chemotherapy in our hospital from August 2016 to January 2018; multiparametric magnetic resonance imaging (contrast-enhanced T1-weighted imaging and diffusion-weighted imaging) and whole slide image of hematoxylin and eosin-stained biopsy sections were collected. Quantitative imaging features were extracted from the multiparametric magnetic resonance imaging and the whole slide image were used to construct a radiopathomics signature model powered by machine learning methods. Models based on multiparametric magnetic resonance imaging or whole slide image alone were also constructed for comparison and referred to as the radiomics signature and pathomics signature models, respectively. Four modeling methods were used to establish prediction models. Model performances were evaluated using receiver operating characteristic curve analysis and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. ResultsThe radiopathomics signature model had favourable performance for the prediction of pathological complete response in the training set (the best value: area under the curve 0.83, accuracy 0.84, and sensitivity 0.87), and in the test set (the best value: area under the curve 0.91, accuracy 0.90, and sensitivity 0.88). In the test set, the radiopathomics signature model also significantly outperformed the radiomics signature (the best value: area under the curve 0.83, accuracy 0.64, and sensitivity 0.62), pathomics signature (the best value: area under the curve 0.60, accuracy 0.74, and sensitivity 0.62) (p > 0.05). Decision curve analysis and calibration curves confirmed the excellent performance of these prediction models in discrimination, calibration, and clinical usefulness. ConclusionsThe results of this study suggest that radiopathomics, the combination of both radiological information regarding the whole tumor and pathological information at the cellular level, could potentially predict discrepancies in pathological response and provide evidence for rational treatment plans.

Prediction of pathological complete response of breast cancer patients who received neoadjuvant chemotherapy with a nomogram based on clinicopathologic variables, ultrasound, and MRI.

To establish a nomogram for predicting the pathologic complete response (pCR) in breast cancer (BC) patients after NAC by applying magnetic resonance imaging (MRI) and ultrasound (US). A total of 607 LABC women who underwent NAC before surgery between January 2016 and June 2022 were retrospectively enrolled, and then were randomly divided into the training (n = 425) and test set (n = 182) with the ratio of 7:3. MRI and US variables were collected before and after NAC, as well as the clinicopathologic features. Univariate and multivariate logistic regression analyses were applied to confirm the potentially associated predictors of pCR. Finally, a nomogram was developed in the training set with its performance evaluated by the area under the receiver operating characteristics curve (ROC) and validated in the test set. Of the 607 patients, 108 (25.4%) achieved pCR. Hormone receptor negativity (odds ratio [OR], 0.3; P < .001), human epidermal growth factor receptor 2 positivity (OR, 2.7; P = .001), small tumour size at post-NAC US (OR, 1.0; P = .031), tumour size reduction ≥50% at MRI (OR, 9.8; P < .001), absence of enhancement in the tumour bed at post-NAC MRI (OR, 8.1; P = .003), and the increase of ADC value after NAC (OR, 0.3; P = .035) were all significantly associated with pCR. Incorporating the above variables, the nomogram showed a satisfactory performance with an AUC of 0.884. A nomogram including clinicopathologic variables and MRI and US characteristics shows preferable performance in predicting pCR. A nomogram incorporating MRI and US with clinicopathologic variables was developed to provide a brief and concise approach in predicting pCR to assist clinicians in making treatment decisions early.

Preoperative systemic inflammation response index: Clinicopathologic predictor of pathological complete response in HER2-positive breast cancer patients receiving neoadjuvant systemic therapy.

Multiple pretreatment systemic inflammatory markers (SIMs) have been reported as predictors of pathological complete response (pCR) after neoadjuvant systemic therapy (NST) in patients with breast cancer (BC). However, the most significant SIM remains to be conclusively identified, and variations among different molecular subtypes remain unknown. The objective of the study was to identify the most significant SIM in patients with human epidermal growth factor receptor 2 (HER2) positive BC, to construct a pCR-predictive nomogram combining it with other clinicopathologic factors, and to evaluate its prognostic value on survival. We retrospectively reviewed the findings for 240 patients with stage I-III HER2-positive BC who underwent NST and subsequent surgery at Kaohsiung and Taichung Veterans General Hospital from 2011 to 2021. Clinicopathologic factors were analyzed by stepwise logistic regression with backward selection. The data were used to construct a nomogram plot for determining the pCR probability. Kaplan-Meier curves and log-rank test were used to evaluate disease-free survival (DFS) and overall survival (OS). Among the pretreatment SIMs, only the systemic inflammation response index (SIRI) was significantly related to pCR, with an optimal cutoff value of 1.27 × 10 9 /L. Stepwise logistic analyses indicated that clinical N stage, HER2 immunohistochemistry score, hormone receptor status, targeted therapy regimen, and SIRI were independent predictors of pCR, with an area under the curve of 0.722. The Hosmer-Lemeshow test and calibration curve revealed that the predictive ability was a good fit to actual observations. A nomogram was constructed based on the logistic model. The external validation of the model also revealed satisfactory discrimination and calibration. Kaplan-Meier analysis showed that patients with SIRI <1.27 had longer DFS and OS. Pretreatment SIRI <1.27 is predictive of pCR, DFS, and OS in HER2-positive BC. Our nomogram could efficiently predict pCR and facilitate clinical decision-making before neoadjuvant treatment.

Breast MRI radiomics and machine learning radiomics-based predictions of response to neoadjuvant chemotherapy - how are they affected by variations in tumour delineation?

Manual delineation of volumes of interest (VOIs) by experts is considered the gold-standard method in radiomics analysis. However, it suffers from inter- and intra-operator variability. A quantitative assessment of the impact of variations in these delineations on the performance of the radiomics predictors is required to develop robust radiomics based prediction models. In this study, we developed radiomics models for the prediction of pathological complete response to neoadjuvant chemotherapy in patients with two different breast cancer subtypes based on contrast-enhanced magnetic resonance imaging acquired prior to treatment (baseline MRI scans). Different mathematical operations such as erosion, smoothing, dilation, randomization, and ellipse fitting were applied to the original VOIs delineated by experts to simulate variations of segmentation masks. The effects of such VOI modifications on various steps of the radiomics workflow, including feature extraction, feature selection, and prediction performance, were evaluated. Using manual tumour VOIs and radiomics features extracted from baseline MRI scans, an AUC of up to 0.96 and 0.89 was achieved for human epidermal growth receptor 2 positive and triple-negative breast cancer, respectively. For smoothing and erosion, VOIs yielded the highest number of robust features and the best prediction performance, while ellipse fitting and dilation lead to the lowest robustness and prediction performance for both breast cancer subtypes. At most 28% of the selected features were similar to manual VOIs when different VOI delineation data were used. Differences in VOI delineation affects different steps of radiomics analysis, and their quantification is therefore important for development of standardized radiomics research.

Neutrophil to lymphocyte ratio does not behave as a good predictor of pathological complete response in breast cancer: A retrospective analysis in the neoadjuvant setting

The breast cancer scenario now requires new solutions for diagnosis and follow-up. The neutrophil-to-lymphocyte ratio (NLR) has been studied as a possible biomarker for predicting prognosis in solid tumors. Due to the ease of obtaining these values from a blood count, NLR could be useful in clinical practice. Therefore, the aim of this study was to analyze the relation between NLR and pathologic complete response (PCR) in breast cancer patients undergoing neoadjuvant treatment. We performed a cross-sectional retrospective study, including ductal breast cancer patients who underwent neoadjuvant chemotherapy and surgery between 2017 and 2019 at the reference hospital institution (n = 1230). Our data were obtained from medical records and laboratory results, so the study was approved by the research ethics committee of the data-providing hospital. Inclusion and exclusion criteria resulted in a final sample of 114 patients. The area under the curve (ROC) showed no statistically significant area (AUC = 0.546) with a CI95% = 0.417–0.676. No relation between NLR and PCR was observed (p = 0.631), indicating that NLR is not a good biomarker for PCR in this population. Regarding the pattern of NLR for different molecular subtypes, no statistical relation was found (p = 0.929). Thus, our study supports the literature that suggests there is no relationship between NLR and PCR.