Predict Chemotherapy Response Research Articles

LINC01764 promotes colorectal cancer cells proliferation, metastasis, and 5‐fluorouracil resistance by regulating glucose and glutamine metabolism via promoting c‐MYC translation

AbstractFew biomarkers are available for predicting chemotherapeutic response and prognosis in colorectal cancer (CRC). Long‐noncoding RNAs (lncRNAs) are essential for CRC development and growth. Therefore, studying lncRNAs may reveal potential predictors of chemotherapy response and prognosis in CRC. LINC01764 was analyzed using datasets from Fudan University Shanghai Cancer Center's advanced CRC patients’ RNA‐seq and The Cancer Genome Atlas datasets. Gene set enrichment analysis was employed to detect related pathways. Cotransfection experiments, RNA pulldown assays, RNA‐binding protein immunoprecipitation, protein synthesis activity, and dual‐luciferase reporter assays were performed to determine interactions among LINC01764, hnRNPK, and c‐MYC. High LINC01764 expression correlates with metastasis, a poor response to FOLFOX/XELOX chemotherapy, and a poor prognosis in CRC. LINC01764 enhance glycolysis and glutamine metabolism to promote CRC cells proliferation, metastasis, and 5‐fluorouracil (5‐FU) resistance. LINC01764 specifically binds to hnRNPK, facilitating its interaction with c‐MYC mRNA and promoting internal ribosome entry site (IRES)‐dependent translation of c‐MYC, thereby exerting oncogenic effects. LINC01764 induced 5‐FU chemoresistance by upregulating the c‐MYC, glucose, and glutamine metabolism pathways, which downregulated UPP1, crucial for activating 5‐FU. Conclusively, LINC01764 promotes CRC progression and 5‐FU resistance through hnRNPK‐mediated‐c‐MYC IRES‐dependent translational regulation, which suggests its potential as a predictor of CRC chemotherapy response and prognosis.

A phenocopy signature of TP53 loss predicts response to chemotherapy

In preclinical studies, p53 loss of function impacts chemotherapy response, but this has not been consistently validated clinically. We trained a TP53-loss phenocopy gene expression signature from pan-cancer clinical samples in the TCGA. In vitro, the TP53-loss phenocopy signature predicted chemotherapy response across cancer types. In a clinical dataset of 3003 breast cancer samples treated with neoadjuvant chemotherapy, the TP53-loss phenocopy samples were 56% more likely to have a pathologic complete response (pCR), with a significant association between TP53-loss phenocopy and pCR in both ER positive and ER negative tumors. In an independent clinical validation in the I-SPY2 trial (N = 987), we confirmed the association with neoadjuvant chemotherapy pCR and found higher rates of chemoimmunotherapy response in TP53-loss phenocopy tumors compared to non-TP53-loss phenocopy tumors (64% vs. 28%). The TP53-loss phenocopy signature predicts chemotherapy response across cancer types in vitro, and in a proof-of-concept clinical validation is associated with neoadjuvant chemotherapy response across multiple clinical breast cancer cohorts.

Evaluating the feasibility and predictive accuracy of biodynamic imaging to platinum-based chemotherapy response in esophageal adenocarcinoma.

Esophageal cancer management lacks reliable response predictors to chemotherapy. In this study we evaluated the feasibility and accuracy of Biodynamic Imaging (BDI), a technology that employs digital holography as a rapid predictor of chemotherapy sensitivity in locoregional esophageal adenocarcinoma. Pre-treatment endoscopic pinch biopsies were collected from patients with esophageal adenocarcinoma during standard staging procedures. BDI analyzed the tumor samples and assessed in vitro chemotherapy sensitivity. BDI sensitivity predictions were compared to patients' pathological responses, the gold standard for determining clinical response, in the surgically treated subset (n=18). BDI was feasible with timely tissue acquisition, collection, and processing in all 30 enrolled patients and successful BDI analysis in 28/29 (96%) eligible. BDI accurately predicted chemotherapy response in 13/18 (72.2%) patients using a classifier for complete, marked, and partial/no-response. BDI technology had 100% negative predictive value for complete pathological response hence identifying patients unlikely to respond to treatment. BDI technology can potentially predict patients' response to platinum chemotherapy. Additionally, this technology represents a promising step towards optimizing treatment strategies for esophageal adenocarcinoma patients by pre-emptively identifying non-responders to conventional platinum-based chemotherapy.

MR radiomics unveils neoadjuvant chemo-responsiveness with insights into selective treatment de-intensification in HPV-positive oropharyngeal carcinoma

BackgroundAccurate prediction of neoadjuvant chemotherapy (NAC) response allows for NAC-guided personalized treatment de-intensification in HPV-positive oropharyngeal squamous cell carcinoma (OPSCC). In this study, we aimed to apply baseline MR radiomic features to predict NAC response to help select NAC-guided de-intensification candidates, and to explore biological underpinnings of response-oriented radiomics. MethodsPre-treatment MR images and clinical data of 131 patients with HPV-positive OPSCC were retrieved from Fudan University Shanghai Cancer Center. Patients were divided into training cohort (n = 47), validation cohort 1 (n = 49) from NAC response-adapted de-intensification trial (IChoice-01, NCT04012502) and real-world validation cohort 2 (n = 35). NAC prediction model using linear support vector machine (SVM) was built and validated. Subsequent nomograms combined radiomics and clinical characteristics were established to predict survival outcomes. RNA-seq and proteomic data were compared to interpret the molecular features underlying radiomic signatures with differential NAC response. FindingsFor NAC response prediction, the fusion model with both oropharyngeal and nodal signatures achieved encouraging performance to predict good responders in the training cohort (AUC 0·89, 95% CI, 0·79-0·95) and validation cohort 1 (AUC 0·71, 95% CI, 0·59-0·83). For prognosis prediction, radiomics-based nomograms exhibited satisfactory discriminative ability between low-risk and high-risk patients (PFS, C-index 0·85, 0·76 and 0·83; OS, C-index 0·79, 0·76 and 0·87, respectively) in three cohorts. Expression analysis unveiled NAC poor responders had predominantly enhanced keratinization while good responders were featured by upregulated immune response and oxidative stress. InterpretationThe MR-based radiomic models and prognostic models efficiently discriminate among patients with different NAC response and survival risk, which help candidate selection in HPV-positive OPSCC with regard to personalized treatment de-intensification.

Plasma Cell-Free DNA Concentration and Fragmentomes Predict Neoadjuvant Chemotherapy Response in Cervical Cancer Patients.

Cervical cancer remains one of the most lethal gynecological malignancies. However, biomarkers for more precise patient care are an unmet need. Herein, the concentration of 285 plasma cell-free DNA (cfDNA) samples are analyzed from 84 cervical patients and the clinical significance of cfDNA fragmentomic characteristics across the neoadjuvant chemotherapy (NACT) treatment. Patients with poor NACT response exhibit a significantly greater escalation in cfDNA levels following the initial cycle of treatment, in comparison to patients with a favorable response. Distinctive end motif profiles and promoter coverages of cfDNA in initial plasma are observed between patients with differing NACT responses. Notably, the DNASE1L3 analysis further demonstrates the intrinsic association between cfDNA characteristics and chemotherapy resistance. The cfDNA and motif ratios show a good discriminative capacity for predicting non-responders from responders (area under the curve (AUC) > 0.8). In addition, transcriptional start sites (TSS) coverages around promoters discern the alteration of biological processes associated with chemotherapy resistance and reflect the potential value in predicting chemotherapy response. These findings in predictive biomarkers may optimize treatment selection, minimize unnecessary treatment, and assist in establishing personalized treatment strategies for cervical cancer patients.

Abstract B013: Host phenotype profiling predicts progression to resection in neoadjuvant pancreatic cancer

Abstract Introduction Neoadjuvant chemotherapy offers a modest survival benefit in pancreatic cancer. Current methods for assessing response to chemotherapy are limited to CA19-9 and post- treatment imaging. However, these methods are not without caveats and focus heavily on tumour biology and staging the tumour, without consideration of host biology or staging the host. We sought to explore the relationship between host biology and outcomes in neoadjuvantly- treated pancreatic cancer, and determine the utility of host phenotype profiling as a tool for predicting chemotherapy response and subsequent progression to resection. Methods Host phenotype profiling was performed by measuring body composition (BC) pre- and post- neoadjuvant chemotherapy in a large international pancreatic cancer cohort (n = 301). Patients with resectable or borderline resectable disease were included if they received chemotherapy with neoadjuvant intent. BC was determined with contrast-enhanced CT scans by measuring single-slice L3 cross-sectional area. Results There were significant differences in BC change from baseline to post-treatment between resected (n = 174) and non-resected patients (n = 127). Patients who did not progress to resection exhibited greater loss in all BC compartments vs those who progressed to resection (muscle area -3.4 vs -0.7, visceral fat -10.1 vs -2.1, subcutaneous fat -12.0 vs -1.4, all p < 0.001, median percentage change per 100 days). We used these values to define a adverse and favourable host phenotypes, which significantly correlated with progression to resection (58.8% adverse phenotype, 74.3% favourable phenotype, p < 0.001). The independence of this effect was confirmed in a logistic regression model with well-established clinico-pathological variables including CA19-19 rise (adverse phenotype OR of non-resection 4.57, 95% CI 1.55-13.48, p = 0.006). Conclusion Host phenotype profiling represents a useful addition to an otherwise limited toolbox of methods for assessing treatment response and selecting for surgery in neoadjuvant pancreatic cancer. Clinicians will be familiar with the subjective “gut feeling” of identifying patients in the clinic who are unlikely to be candidates for surgery, and we demonstrate an objective measurement of this. Early identification of patients on an adverse trajectory may highlight those who would benefit from early intervention such as prehabilitation, salvage change of chemotherapy regimen, or nutritional optimization to switch the phenotype from adverse to favourable and reverse adverse host biology. Citation Format: Adam Bryce, Stephan Dreyer, Ross Dolan, Fieke Froeling, Dario Solinas, Alice Cattelani, Antonio Pea, David Chang. Host phenotype profiling predicts progression to resection in neoadjuvant pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr B013.

Single-cell analysis of chemotherapy-resistant microenvironment identifies a chemo-response biomarker for pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a highly fatal, aggressive cancer due to limited response chemotherapy. The tumor microenvironment (TME) has emerged as a key player in the development of chemoresistance and in malignant progression. In this study, we hypothesized that chemotherapy response is predictable by chemotherapy-related cell types and their differentially expressed genes (DEGs). DEGs of chemoresistance cell types were identified via single cell analysis and Wilcoxon test. A chemotherapy response signature was established using a random forest model and validated with public datasets. Bulk cell fraction was analyzed using BayesPrism algorithms. Log-rank test was used to analyze survival of PDAC patients. We found that natural killer (NK) cells, myeloid cells, and erythroid cells were highly infiltrated in chemo-resistant TME. A total of 36 chemoresistance-related DEGs of chemo-resistant cells were identified in chemo-resistant PDAC. Functional enrichment analysis showed that chemoresistance upregulated various inflammation-related pathways, including TGF-β signaling. Based on these features, we constructed a random forest model to predict the response and survival for PDAC patients, which accurately distinguished high-risk and chemoresistant patients with significantly poorer prognosis in both the training and independent validation datasets. Cox regression analysis indicated that predicted labels were an independent prognostic factor in PDAC. Moreover, deconvolution of TME confirmed higher infiltration levels of M2 macrophage and NK cells in predicted chemoresistance. When combined with chemotherapy response related tumor mutations, the model showed excellent ability in predicting chemotherapy response and survival. The TME was closely associated with the chemotherapy response and prognosis of PDAC. Our TME-based random forest model predicted chemotherapy response with complementary knowledge to the response-related genetic mutations.

DCTPP1 Expression as a Predictor of Chemotherapy Response in Luminal A Breast Cancer Patients.

Breast cancer (BRCA) remains a significant global health challenge due to its prevalence and lethality, exacerbated by the development of resistance to conventional therapies. Therefore, understanding the molecular mechanisms underpinning chemoresistance is crucial for improving therapeutic outcomes. Human deoxycytidine triphosphate pyrophosphatase 1 (DCTPP1) has emerged as a key player in various cancers, including BRCA. DCTPP1, involved in nucleotide metabolism and maintenance of genomic stability, has been linked to cancer cell proliferation, survival, and drug resistance. This study evaluates the role of DCTPP1 in BRCA prognosis and chemotherapy response. Data from the Cancer Genome Atlas Program (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) repositories, analyzed using GEPIA and Kaplan-Meier Plotter, indicate that high DCTPP1 expression correlates with poorer overall survival and increased resistance to chemotherapy in BRCA patients. Further analysis reveals that DCTPP1 gene expression is up-regulated in non-responders to chemotherapy, particularly in estrogen receptor (ER)-positive, luminal A subtype patients, with significant predictive power. Additionally, in vitro studies show that DCTPP1 gene expression increases in response to 5-fluorouracil and doxorubicin treatments in luminal A BRCA cell lines, suggesting a hypothetical role in chemoresistance. These findings highlight DCTPP1 as a potential biomarker for predicting chemotherapy response and as a therapeutic target to enhance chemotherapy efficacy in BRCA patients.

Allysine-Targeted Molecular MRI Enables Early Prediction of Chemotherapy Response in Pancreatic Cancer.

Neoadjuvant therapy is routinely used in pancreatic ductal adenocarcinoma (PDAC), but not all tumors respond to this treatment. Current clinical imaging techniques are not able to precisely evaluate and predict the response to neoadjuvant therapies over several weeks. A strong fibrotic reaction is a hallmark of a positive response, and during fibrogenesis, allysine residues are formed on collagen proteins by the action of lysyl oxidases. Here, we report the application of an allysine-targeted molecular MRI probe, MnL3, to provide an early, noninvasive assessment of treatment response in PDAC. Allysine increased 2- to 3-fold after one dose of neoadjuvant therapy with FOLFIRINOX in sensitive human PDAC xenografts in mice. Molecular MRI with MnL3 could specifically detect and quantify fibrogenesis in PDAC xenografts. Comparing the MnL3 signal before and 3 days after one dose of FOLFIRINOX predicted subsequent treatment response. The MnL3 tumor signal increased by 70% from day 0 to day 3 in mice that responded to subsequent doses of FOLFIRINOX, whereas no signal increase was observed in FOLFIRINOX-resistant tumors. This study indicates the promise of allysine-targeted molecular MRI as a noninvasive tool to predict chemotherapy outcomes. Significance: Allysine-targeted molecular MRI can quantify fibrogenesis in pancreatic tumors and predict response to chemotherapy, which could guide rapid clinical management decisions by differentiating responders from nonresponders after treatment initiation.

A susceptibility gene signature for ERBB2-driven mammary tumour development and metastasis in collaborative cross mice

Deeper insights into ERBB2-driven cancers are essential to develop new treatment approaches for ERBB2+ breast cancers (BCs). We employed the Collaborative Cross (CC) mouse model to unearth genetic factors underpinning Erbb2-driven mammary tumour development and metastasis. 732 F1 hybrid female mice between FVB/N MMTV-Erbb2 and 30 CC strains were monitored for mammary tumour phenotypes. GWAS pinpointed SNPs that influence various tumour phenotypes. Multivariate analyses and models were used to construct the polygenic score and to develop a mouse tumour susceptibility gene signature (mTSGS), where the corresponding human ortholog was identified and designated as hTSGS. The importance and clinical value of hTSGS in human BC was evaluated using public datasets, encompassing TCGA, METABRIC, GSE96058, and I-SPY2 cohorts. The predictive power of mTSGS for response to chemotherapy was validated invivo using genetically diverse MMTV-Erbb2 mice. Distinct variances in tumour onset, multiplicity, and metastatic patterns were observed in F1-hybrid female mice between FVB/N MMTV-Erbb2 and 30 CC strains. Besides lung metastasis, liver and kidney metastases emerged in specific CC strains. GWAS identified specific SNPs significantly associated with tumour onset, multiplicity, lung metastasis, and liver metastasis. Multivariate analyses flagged SNPs in 20 genes (Stx6, Ramp1, Traf3ip1, Nckap5, Pfkfb2, Trmt1l, Rprd1b, Rer1, Sepsecs, Rhobtb1, Tsen15, Abcc3, Arid5b, Tnr, Dock2, Tti1, Fam81a, Oxr1, Plxna2, and Tbc1d31) independently tied to various tumour characteristics, designated as a mTSGS. hTSGS scores (hTSGSS) based on their transcriptional level showed prognostic values, superseding clinical factors and PAM50 subtype across multiple human BC cohorts, and predicted pathological complete response independent of and superior to MammaPrint score in I-SPY2 study. The power of mTSGS score for predicting chemotherapy response was further validated in an invivo mouse MMTV-Erbb2 model, showing that, like findings in human patients, mouse tumours with low mTSGS scores were most likely to respond to treatment. Our investigation has unveiled many new genes predisposing individuals to ERBB2-driven cancer. Translational findings indicate that hTSGS holds promise as a biomarker for refining treatment strategies for patients with BC. The U.S. Department of Defense (DoD) Breast Cancer Research Program (BCRP) (BC190820), United States; MCIN/AEI/10.13039/501100011039 (PID2020-118527RB-I00, PDC2021-121735-I00), the "European Union Next Generation EU/PRTR," the Regional Government of Castile and León (CSI144P20), European Union.

The value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in the prediction of neoadjuvant chemotherapy response in breast cancer: A Meta-Analysis

The value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in the prediction of neoadjuvant chemotherapy response in breast cancer: A Meta-Analysis

Can Ultrasound Evaluation of Lymph Node Size and Necrosis Rate Predict Chemotherapy Response in Cervical Tuberculous Lymphadenitis?

To explore the relationship between the initial size and the necrotic rate of lymph nodes evaluated by ultrasound in patients with cervical tuberculous lymphadenitis (CTL) and therapeutic response. Overall, 55 patients were included in this study. Conventional ultrasound and contrast-enhanced ultrasound examination were performed before anti-tuberculosis chemotherapy. Based on the different therapeutic outcomes, they were divided into responder groups (n = 39) and non-responder groups (n = 16). The relationship between the initial size (maximum area, length diameter, short diameter), rate of necrosis, and therapeutic response were compared and analyzed between two groups. There was a significant difference in maximum area, short diameter and rate of necrosis of lymph nodes between the responder groups and the non-responder groups (P < 0.05). The receiver-operating-characteristic (ROC) curve analysis was used to differentiate the two groups, it showed that the area under the curve was 0.746 for maximum area and 0.721 for short diameter, respectively. The cut-off value for the lymph node maximum area and short diameter based on ROC curve analysis was determined as 3.94cm2 (sensitivity 76.9%, specificity 68.7%) and 1.15cm (sensitivity 59.0%, specificity 93.7%), respectively. A negative correlation was observed between maximum area, short diameter, and therapeutic response. The initial maximum area and short diameter of lymph nodes were found to have a negative correlation with chemotherapy response in patients with CTL. The treatment outcomes are typically unsatisfactory for lymph nodes exhibiting an initial necrosis rate of 50% or higher. These findings may be helpful for evaluating therapeutic response.

A computed tomography-based nomogram for neoadjuvant chemotherapy plus immunotherapy response prediction in patients with advanced esophageal squamous cell carcinoma.

To develop a CT-based nomogram to predict the response of advanced esophageal squamous cell carcinoma (ESCC) to neoadjuvant chemotherapy plus immunotherapy. In this retrospective study, 158 consecutive patients with advanced ESCC receiving contrast-enhanced CT before neoadjuvant chemotherapy plus immunotherapy were randomized to a training cohort (TC, n = 121) and a validation cohort (VC, n = 37). Response to treatment was assessed with response evaluation criteria in solid tumors. Patients in the TC were divided into the responder (n = 69) and non-responder (n = 52) groups. For the TC, univariate analyses were performed to confirm factors associated with response prediction, and binary analyses were performed to identify independent variables to develop a nomogram. In both the TC and VC, the nomogram performance was assessed by area under the receiver operating characteristic curve (AUC), calibration slope, and decision curve analysis (DCA). In the TC, univariate analysis showed that cT stage, cN stage, gross tumor volume, gross volume of all enlarged lymph nodes, and tumor length were associated with the response (all P < 0.05). Binary analysis demonstrated that cT stage, cN stage, and tumor length were independent predictors. The independent factors were imported into the R software to construct a nomogram, showing the discriminatory ability with an AUC of 0.813 (95% confidence interval: 0.735-0.890), and the calibration curve and DCA showed that the predictive ability of the nomogram was in good agreement with the actual observation. This study provides an accurate nomogram to predict the response of advanced ESCC to neoadjuvant chemotherapy plus immunotherapy.

8P Biomarkers to predict chemotherapy response in low-grade serous ovarian carcinoma

8P Biomarkers to predict chemotherapy response in low-grade serous ovarian carcinoma

Abstract PS05-02: PET Imaging of PARP Expression as a Biomarker of Response to Chemotherapy in Breast Cancer: A Nonrandomized Clinical Trial

Abstract Introduction: Poly–(adenosine diphosphate–ribose) polymerase (PARP) proteins are involved in double-stranded break repair, mediated through homologous recombination (HR) and non-homologous end joining (NHEJ). The majority of PARP activity in this context is attributed to PARP-1 (85-90%). PARP-1 binding to damaged DNA, catalytic activity, and subsequent release to allow access for other DNA repair proteins are necessary for efficient DNA repair. The critical function of PARP-1 in these processes makes this protein a unique potential biomarker of DNA repair capability. [18F]FluorThanatrace ([18F]FTT) is an 18F-labeled PARP inhibitor analog that binds to the same site as approved PARPi drugs. We hypothesized that the biologically relevant form of PARP in untreated breast cancer might provide an indication of functional tumor DNA repair capabilities and therefore predict chemotherapy response. Thus, this study aimed to determine whether a non-invasive quantitative measure of in vivo PARP expression correlated with response to chemotherapy in breast cancer. Methods: A single-arm prospective trial enriched for subjects with triple negative breast cancer (TNBC) was conducted at the University of Pennsylvania from May 2017 to March 2022 (NCT03083288 and NCT03846167). Inclusion criteria were primary breast cancer with tumor diameter of at least 1 cm on conventional imaging and willingness to undergo an [18F]FTT-PET scan prior to neoadjuvant chemotherapy. Participants provided written informed consent. [18F]FTT uptake in breast cancer was measured pre-therapy and tested for association with pathologic complete response (pCR). Subjects were scanned on an Ingenuity TF PET/CT (Philips Healthcare) following injection of [18F]FTT using a 20-minute static acquisition. Peak 1 cm3 average uptake (SUVpeak) were recorded from a spherical region-of-interest (ROI) covering the primary malignancy, guided by CT and prior imaging studies. All SUVs were partial volume corrected and normalized using muscle (NM). A Mann-Whitney test was used to determine if [18F]FTT uptake differed by pCR. Receiver operating characteristic (ROC) curves were constructed to test the performance of [18F]FTT uptake in predicting pCR. Statistical analyses utilized STATA v15.1 with significance based on a two-sided alpha level of ≤0.05. Participants: Twenty-six women with stage I-III breast cancer met inclusion criteria with age range of 29-74 years and self-reported race as Asian (1, 4%), Black (9, 37%) or White (16, 60%). One subject had bilateral tumors (ER+ stage IIA and HER2+ stage I) that were evaluated separately (27 total tumors). Breast tumors were pathologically confirmed and included 6 (22%) ER+/HER2-, 5 (19%) HER2+, and 16 (59%) TNBC. Germline sequencing was available for 21 participants. Results: Mean tumor diameter ranged from 15 to 91 mm (median 30 mm). A considerable range of [18F]FTT uptake was seen across subjects (SUVpeak/NM 0.66-6.5). [18F]FTT uptake was independent of subtype (ER+, HER2+, TN) (P=0.35), stage (P=0.39), and between mutations in genes associated with homologous recombination deficiency (i.e., BRCA1/2, PALB2) and wild-type (P=0.73). In the TNBC cohort, pre-treatment [18F]FTT uptake was higher in subjects who went on to have a pCR (n=16, P=0.05), while a trend toward higher uptake was seen when including all tumor types (n=27, P=0.11). ROC analysis of the value of [18F]FTT uptake for predicting pCR revealed an AUC of 0.79 and showed that a threshold SUV ratio &amp;lt; 2.47 predicted pCR in TNBC patients with a 100% specificity and 64% sensitivity. Conclusions: These early clinical results suggest a relationship between [18F]FTT uptake, a measure of PARP expression, and chemotherapy sensitivity in TNBC, warranting future studies to elucidate underlying mechanistic processes and potential clinical implications. Citation Format: Sarah Gitto, Anthony Young, Ira Bleiweiss, Amy Clark, Elizabeth McDonald. PET Imaging of PARP Expression as a Biomarker of Response to Chemotherapy in Breast Cancer: A Nonrandomized Clinical 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 PS05-02.

Apriori prediction of chemotherapy response in locally advanced breast cancer patients using CT imaging and deep learning: transformer versus transfer learning.

Neoadjuvant chemotherapy (NAC) is a key element of treatment for locally advanced breast cancer (LABC). Predicting the response to NAC for patients with Locally Advanced Breast Cancer (LABC) before treatment initiation could be beneficial to optimize therapy, ensuring the administration of effective treatments. The objective of the work here was to develop a predictive model to predict tumor response to NAC for LABC using deep learning networks and computed tomography (CT). Several deep learning approaches were investigated including ViT transformer and VGG16, VGG19, ResNet-50, Res-Net-101, Res-Net-152, InceptionV3 and Xception transfer learning networks. These deep learning networks were applied on CT images to assess the response to NAC. Performance was evaluated based on balanced_accuracy, accuracy, sensitivity and specificity classification metrics. A ViT transformer was applied to utilize the attention mechanism in order to increase the weight of important part image which leads to better discrimination between classes. Amongst the 117 LABC patients studied, 82 (70%) had clinical-pathological response and 35 (30%) had no response to NAC. The ViT transformer obtained the best performance range (accuracy = 71 ± 3% to accuracy = 77 ± 4%, specificity = 86 ± 6% to specificity = 76 ± 3%, sensitivity = 56 ± 4% to sensitivity = 52 ± 4%, and balanced_accuracy=69 ± 3% to balanced_accuracy=69 ± 3%) depending on the split ratio of train-data and test-data. Xception network obtained the second best results (accuracy = 72 ± 4% to accuracy = 65 ± 4, specificity = 81 ± 6% to specificity = 73 ± 3%, sensitivity = 55 ± 4% to sensitivity = 52 ± 5%, and balanced_accuracy = 66 ± 5% to balanced_accuracy = 60 ± 4%). The worst results were obtained using VGG-16 transfer learning network. Deep learning networks in conjunction with CT imaging are able to predict the tumor response to NAC for patients with LABC prior to start. A ViT transformer could obtain the best performance, which demonstrated the importance of attention mechanism.

Pathway-based signatures predict patient outcome, chemotherapy benefit and synthetic lethal dependencies in invasive lobular breast cancer.

Invasive Lobular Carcinoma (ILC) is a morphologically distinct breast cancer subtype that represents up to 15% of all breast cancers. Compared to Invasive Breast Carcinoma of No Special Type (IBC-NST), ILCs exhibit poorer long-term outcome and a unique pattern of metastasis. Despite these differences, the systematic discovery of robust prognostic biomarkers and therapeutically actionable molecular pathways in ILC remains limited. Pathway-centric multivariable models using statistical machine learning were developed and tested in seven retrospective clinico-genomic cohorts (n = 996). Further external validation was performed using a new RNA-Seq clinical cohort of aggressive ILCs (n = 48). mRNA dysregulation scores of 25 pathways were strongly prognostic in ILC (FDR-adjusted P < 0.05). Of these, three pathways including Cell-cell communication, Innate immune system and Smooth muscle contraction were also independent predictors of chemotherapy response. To aggregate these findings, a multivariable machine learning predictor called PSILC was developed and successfully validated for predicting overall and metastasis-free survival in ILC. Integration of PSILC with CRISPR-Cas9 screening data from breast cancer cell lines revealed 16 candidate therapeutic targets that were synthetic lethal with high-risk ILCs. This study provides interpretable prognostic and predictive biomarkers of ILC which could serve as the starting points for targeted drug discovery for this disease.

Abstract LB096: Telomere length as a predictor of therapy response and survival in patients diagnosed with ovarian carcinoma

Abstract Impaired telomere length (TL) maintenance in ovarian tissue may play a pivotal role in epithelial ovarian carcinoma (OvC) onset. TL in either target or surrogate tissue (blood) is currently being investigated as a predictor in anti-OvC therapy or as a biomarker of the disease progression, respectively. There is presently an urgent need for an appropriate approach to chemotherapy response prediction. We performed a monochrome multiplex qPCR measurement of TL in peripheral blood and tumor tissues of 209 OvC patients. Methylation status and gene expression of shelterin complex and telomerase catalytic subunit (hTERT) were determined within tumor tissues by High-Throughput DNA methylation profiling and RNA sequencing (RNA-Seq) analysis, respectively. The patients sensitive to cancer treatment (n=46) had shorter telomeres in peripheral blood cells compared to those treatment-resistant (n=93; P=0.037). Cancer tissue TL lower than the median predisposed to better overall survival (P=0.002). Gene expression of TPP1, which recruits telomerase to telomeres, was positively associated with TL in tumor tissue (P=0.026). TL measured in peripheral blood cells could serve as a marker of platinum therapy response in OvC patients. Additionally, TL determined in tumor tissue provides information on OvC patients' overall survival. The study was financed by the project NPO (LX22NPO05102), the Czech Health Research Council (NU21-03-00145), and the Czech Science Foundation (21-27902S). Citation Format: Kristyna Tomasova, Karolina Seborova, Michal Kroupa, Josef Horak, Ludmila Vodickova, Lukas Rob, Martin Hruda, Marcela Mrhalova, Alena Bartakova, Jiri Bouda, Thomas Fleischer, Vessela Kristensen, Pavel Vodicka, Radka Vaclavikova. Telomere length as a predictor of therapy response and survival in patients diagnosed with ovarian carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB096.

Abstract LB242: Prediction of patient response to neoadjuvant chemotherapy in breast cancer from their deconvolved tumor microenvironment transcriptome

Abstract Introduction: The tumor microenvironment (TME) is a complex and dynamic ecosystem that plays critical roles in tumor development and clinical outcome. While the multifarious cellular interactions within the TME have been extensively studied with a significant emphasis on immunotherapy, understanding its role in chemotherapy outcome remains less explored. To this end, we present a novel generic computational framework named DECODEM (DEcoupling Cell-type-specific Outcomes using DEconvolution and Machine learning). DECODEM facilitates the exploration of the association of cell-type-specific gene expression profiles with the effectiveness of neoadjuvant chemotherapy (NAC) in treating breast cancer (BC). This approach holds promise for elucidating the nuanced ways in which the TME can modulate therapeutic efficacy, potentially paving the way for more personalized and effective cancer treatments. Methods: DECODEM employs cellular deconvolution of bulk transcriptomics to extract cell-type-specific gene expression profiles within the TME, and subsequently harnesses machine learning to construct cell-type-specific predictors of clinical outcomes. Utilizing DECODEM, we analyzed three publicly available cohorts of HER2-negative BC patients who received NAC. We assessed the predictive capabilities of the individual cell types as well as their collective influence, benchmarking against a state-of-the-art predictor that employs both clinical and transcriptomic features. We corroborated our findings in a single-cell (SC) transcriptomics cohort of patients with triple negative breast cancer (TNBC) undergoing chemotherapy. Further, we expanded DECODEM to DECODEMi, which accounts for cell-cell interactions (CCIs) among the predominant cell types, offering potential insights into the mechanistic processes governing clinical responses. Results: Our analysis indicated that the heterogeneous composition of the TME is crucial for chemotherapy response prediction in HER2-negative BC, beyond the influence of malignant cells alone. The key insights from our study are as follows:1. Gene expression of immune cells (myeloid, plasmablasts, B-cells) and stromal cells (endothelial, normal epithelial, cancer-associated fibroblasts) displayed comparable-to-superior predictive powers over both bulk expression and the state-of-the-art predictor.2. The predictive accuracy is further augmented by multi-cell-type ensembles, with a triad ensemble comprising endothelial, myeloid, and plasmablast cells achieving the highest predictive accuracy across multiple cohorts.3. DECODEMi can effectively capture the regulatory impact of CCIs (validated in a SC dataset sourced from the SC-TNBC cohort) and has identified novel CCIs likely to influence chemotherapy response in HER2-negative BC. Conclusion: We provide an unprecedented computational methodology to quantitatively assess the cell-type-specific influences of the TME on clinical outcome. Our analysis offers insights into the complex interactions among TME cells and their correlation with chemotherapy efficacy in HER2-negative BC, thereby enriching the knowledge base of BC treatment effectiveness. Citation Format: Saugato Rahman Dhruba, Sahil Sahni, Binbin Wang, Di Wu, Yael Schmidt, Eldad Shulman, Sanju Sinha, Stephen-John Sammut, Carlos Caldas, Kun Wang, Eytan Ruppin. Prediction of patient response to neoadjuvant chemotherapy in breast cancer from their deconvolved tumor microenvironment transcriptome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB242.

Abstract 7609: Potential gene expression meta-signatures predict neoadjuvant chemotherapy response in invasive breast cancer

Abstract Early prediction of Neoadjuvant Chemotherapy (NAC) response is crucial for women with locally advanced breast cancer (BC), given their low response rates. This study aims to identify gene expression patterns and tumor microenvironment (TME) characteristics associated with resistance to NAC, which could significantly impact tumor progression and treatment outcomes. The study encompassed differential gene expression comparing non-responders and responders, enrichment analysis, tumor microenvironment estimation, and cytokines identification from a female patient's cohort with distinct molecular subtypes and candidates to NAC at the Colombian National Cancer Institute. Candidate differentially expressed genes validated with RT-PCR in a larger validation cohort and corroborated in several publicly available databases, including associated treatment drugs. Distinct gene expression profiles were observed between responders and non-responders, emphasizing immune system-related pathways such as IL-17 signaling, B cell receptor signaling, complement and coagulation cascades, natural killer cell-mediated cytotoxicity, and NF-kB signaling. Immune-related genes (AGTR1, APOD, CCL19, CGA, ECEL1, NPY1R, NTNG1, PCSK1, PFDN2, S100B, TPRG1, WDR5B, ZNF385B) were identified as biomarkers correlated with non-response and worse prognosis. Responders showed increased B cell populations, CD4+ and CD8+ effector memory T-cells in lymphoid groups, while non-responders displayed elevated levels of CD8+ naive T-cells. Additionally, responders revealed upregulated myeloid cell populations like macrophages and dendritic cells. Several cytokines like IL1B, IL-2, IL-4, IL-12, IL-21, IL-22, IL-36, and TWEAK showed elevated expression in non-responders. Dexamethasone, Vinblastine, and Streptozocin were candidate drugs that interacted with AGTR1, CGA, and S100B, respectively and are associated with progression and metastasis. Logistic regression revealed that overexpression of CGA showed a higher probability (OR:1.83, p=0.03) while ECEL1 had a lower probability (OR=0.44, p=0.04) in non-responders compared to responders who lacked expression of these genes. Immune-related genes and various subtypes of immune cells within the TME exhibit correlations with response to NAC, and several cell fractions significantly changed before and after NAC. These results suggest meta-signatures that warrant further evaluation as concordance assessment between immunohistochemistry (IHC) and gene expression profiling, in addition to an extensive validation to triage patients before NAC and target personalized interventions. Detection and evaluation of TME components are essential to predict NAC efficacy and avoid disease relapse. Finally, these findings emphasize the importance of assessing admixed populations to facilitate novel findings and alleviate disparities. Citation Format: Hedda Michelle Guevara-Nieto, Rafael S. Parra-Medina, Jovanny Zabaleta, Liliana Lopez-Kleine, Alba L. Combita. Potential gene expression meta-signatures predict neoadjuvant chemotherapy response in invasive breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7609.