Predictor Of Objective Response Research Articles

P156 Prostate-specific antigen delta optimal thresholding for predicting objective response in men with de novo metastatic castration-sensitive prostate cancer treated with triplet regimen according to PEACE-1 trial

P156 Prostate-specific antigen delta optimal thresholding for predicting objective response in men with de novo metastatic castration-sensitive prostate cancer treated with triplet regimen according to PEACE-1 trial

Alpha-fetoprotein and des-gamma-carboxy prothrombin can predict the objective response of patients with hepatocellular carcinoma receiving durvalumab plus tremelimumab therapy.

Durvalumab plus tremelimumab (Durva/Treme) combined immunotherapy is the first-line therapy recommended for unresectable hepatocellular carcinoma (HCC). Since sequential therapy is more effective in improving prognosis, tumor markers have been used as predictive biomarkers for response to systemic therapy. This study aimed to investigate the predictive ability of objective response (OR) by tumor markers for Durva/Treme therapy against HCC. In this multicenter study, 110 patients with HCC who received Durva/Treme therapy were retrospectively enrolled. The OR rate was 15.5%. To aid early decision-making regarding OR, we evaluated the predictors contributing to OR in two steps: before (first step) and 4 weeks after (second step) treatment induction. Changes in tumor markers (alpha-fetoprotein [AFP] and des-gamma-carboxy prothrombin [DCP]) from baseline to 4 weeks after treatment (ΔAFP/ΔDCP) were included as the input factors. In the first step, multivariable analysis identified only the baseline AFP level (odds ratio 3.497, p = 0.029) as a predictor of OR. Patients with AFP ≥ 400 ng/mL had a significantly higher OR rate than those with < 400 ng/mL (28.2 vs. 8.5%, p = 0.011), and there was no significant difference in progression-free survival (PFS) between the two groups. When AFP/DCP response was defined as a ≥10% reduction from baseline, multivariable analysis showed that AFP response (odds ratio 6.023, p = 0.042) and DCP response (odds ratio 11.657, p = 0.006) were both independent predictors of OR in the second step. The PFS of patients with AFP or DCP response was significantly longer than that of patients without AFP or DCP response. The study demonstrated that the use of AFP and DCP can predict the OR of patients with HCC receiving Durva/Treme therapy.

External Validation Demonstrates Machine Learning Models Outperform Human Experts in Prediction of Objective and Patient-Reported Overactive Bladder Treatment Outcomes

ObjectivesTo predict treatment response for overactive bladder (OAB) for a specific patient remains elusive. We sought to develop accurate models using machine learning for prediction of objective and patient-reported treatment response to intravesical botulinum toxin (OBTX-A) injection. We sought to validate the models in a challenging setting using an external dataset of a markedly different patient cohort and dosing regimen. We hypothesized the model would outperform human experts and top available algorithms. MethodsAlgorithms using “operator splitting” designed for accuracy and efficiency even in small training data sets with variable completeness, were trained to predict objective response and patient-reported symptomatic improvement using the ROSETTA trial cohort and validated using the ABC trial cohort of patients who underwent OBTX-A. Areas under the curve (AUC) of algorithms were compared to the top publicly-available machine learning classifier XGBoost, logistic regression with cross validation, and human expert predictions in the external validation set. ResultsIn the validation set, the operator splitting neural network had AUC 0.66 and outperformed XGBoost with DART (top available machine learning classifier, AUC 0.58), logistic regression (AUC 0.55), and human experts (AUC 0.47 – 0.53) for prediction of clinical responder status. It was similarly accurate in prediction of patient subjective improvement in symptoms following OBTX-A (AUC: 0.64), again outperforming other algorithms and human experts (AUC 0.41 – 0.62). ConclusionsThe neural network outperformed human experts and other machine learning approaches in prediction of objective and patient-reported OBTX-A outcomes for overactive bladder in a challenging independent validation cohort. Clinical implementation could improve counseling and treatment selection.

Use of artificial intelligence–based digital pathology to predict outcomes for immune checkpoint inhibitor therapy in advanced gastro-esophageal cancer.

4013 Background: Accurate prediction of response and survival outcomes with anti-PD-1/PD-L1 immune checkpoint inhibition (ICI) remains a significant challenge in gastro-esophageal cancers. In this study, we use an artificial intelligence (AI)-based single-cell analysis of digitized whole-slide H&amp;E images (WSIs) to predict objective response and survival benefit of ICI in two independent cohorts of gastro-esophageal cancer patients. Methods: WSIs were obtained from 82 ICI-treated advanced gastroesophageal cancer patients (gastric, esophageal, and gastro-esophageal junction adenocarcinoma and squamous cell carcinoma) at Stanford University for discovery, and 189 ICI-treated advanced gastric adenocarcinoma patients at Southern Medical University for external validation. Deep learning models were deployed for automated tumor area detection and segmentation of cell nuclei within WSIs. We developed a fully automated cell annotation approach by leveraging multiplex immunofluorescence and trained a deep learning model to classify nuclei into four cell types from H&amp;E (tumor cells, lymphocytes, neutrophils, and macrophages). A total of 66 features were computed to quantify cell composition and cell-cell interactions within the tumor microenvironment. Treatment outcomes were assessed using progression-free survival (PFS) and best objective response per the Response Evaluation Criteria in Solid Tumors (v1.1), with statistical significance reported at the 95% confidence level. Results: In the training cohort, a spatial feature characterizing lymphocyte and neutrophil interaction had the strongest association with PFS (hazard ratio = 0.44, 95% CI 0.24-0.79, P = 0.0046). In the validation cohort, the spatial biomarker positive population had significantly improved PFS compared to the spatial biomarker negative population (hazard ratio = 0.41, 95% CI 0.27-0.61, P &lt; 0.0001; median time to event: 19 months vs 9 months). For predicting objective response in the validation cohort, a multivariate model combining the spatial features achieved AUROC = 0.81 compared to AUROC = 0.65 for PD-L1 CPS (P = 0.0014), while combining the multivariate model with PD-L1 CPS achieved AUROC = 0.84. Conclusions: A single-cell computational pathology approachidentifies spatial biomarkers with predictive utility for determining ICI treatment outcomes in advanced gastro-esophageal cancer. Further validation of these findings is being pursued in additional cohorts.

Clinical factors predicting objective response to bevacizumab-based chemotherapies in advanced and recurrent epithelial ovarian cancer.

Bevacizumab-based chemotherapies are commonly administered in the treatment of patients diagnosed with epithelial ovarian cancer (EOC). The primary aim of this study was to assess the factors that predict the objective response to bevacizumab-based therapies in cases of advanced and recurrent EOC. The retrospective data of 264 patients with EOC from the current study were collected between 2009 and 2022 at our clinic. Survival analyses were conducted utilizing the Kaplan-Meier method and the log-rank test. Binary logistic regression analysis was employed to assess the factors predicting the objective response. A predominant subset of patients (83%) presented with serous adenocarcinoma, exhibiting a high-grade differentiation at 87%. The vast majority (80%) of the cohort experienced disease recurrence. Three-fourths of the cases received bevacizumab in combination with platinum-based doublet chemotherapy. In the multivariate analysis, clinical factors such as a disease recurrence (P=0.031), upfront tumor debulking surgery before bevacizumab (P=0.009), doublet chemotherapy (P=0.003), and the presence of malignant pleural effusion (P=0.024) emerged as significant determinants influencing the Objective Response Rate (ORR) in patients undergoing bevacizumab-based therapy. The ORR was 67.5% (N.=178), comprising 15.2% complete responses (N.=40) and 52.1% partial responses (N.=138). The median Progression-Free Survival (PFS) and Overall Survival (OS) were estimated at 10.2 months (95% CI, 8.60-11.9) and 20.1 months (95% CI, 16.0-24.2), respectively. The responses to bevacizumab-based chemotherapies could be predict by the presence of malignant pleural effusion, disease recurrence, upfront tumor debulking surgery and doublet regimen of chemotherapy.

Association of combination tumor texture and vessel tortuosity with progression free survival across PD-L1 subgroups in durvalumab treated non-small cell lung cancer (NSCLC): Blinded validation results from CP1108.

8600 Background: Immunotherapy (IO) has shown a durable response with minimal toxicity in the treatment of metastatic NSCLC. However, only 30-50% of these patients (pts) actually respond. Despite initial optimism surrounding PD-L1 expression as a potential biomarker, IO provides benefit across PD-L1 low (&lt;25%) and high (≥25%) status. This highlights a critical need for more effective biomarkers to guide treatment decisions. Here, we present blinded validation results of a CT-based biomarker of change in quantitative vessel tortuosity (1) (Δ-QVT) and texture radiomics (2) (Δ-Rad) between baseline (B) and 6 weeks post-treatment (TP1) for predicting response, overall survival (OS) and progression free survival (PFS) in CP1108 (NCT01693562). Methods: CT scans at B and TP1 were retrospectively analyzed from two studies (a) a multi-center training set (Str, N=110) of metastatic NSCLC pts treated with first line IO and (b) a blinded NSCLC cohort of N = 151 pts treated with Durvalumab in CP1108 study (Sv). In Sv, N=75 pts were PD-L1 high (Sv+) whereas N=65 pts were PD-L1 low (Sv-). An in-house MATLAB based algorithm was used to extract intra-tumoral, peri-tumoral texture and QVT features from up to two largest measurable tumors in each CT. A classifier (MCombo) was trained on Str to predict best overall response defined as per the RECIST v1.1 criteria using combination of Δ-QVT and Δ-Rad features. Area under the receiver operating characteristic (AUC) was used to evaluate MCombo against RECIST response whereas univariable and multivariable Cox regression models with log-rank test, hazard ratio (HR) with confidence interval (CI) and concordance index (C-index) were used to study the association of MCombo with OS and PFS in Sv, Sv+, and Sv-. Results: MCombo predicted objective response with an AUC of 0.78 in Sv and was statistically significantly associated with PFS (Table) in Sv, Sv+, and Sv-. MCombo was associated with OS in Sv, Sv+, but not in Sv-. Multivariable analysis with PD-L1 status, histological subtype, ECOG status, liver metastasis, line of therapy and sex showed independent association of MCombo across OS and PFS. Conclusions: Our results indicate that combination of radiomics and vessel tortuosity biomarker from CT is independently prognostic of response and PFS across PD-L1 levels. Prospective validation of this biomarker is needed. 1. Alilou et al. Sci Adv 2022. 2. Khorrami et al. Cancer Immunol Res 2020. [Table: see text]

Contribution of tumour and immune cells to PD-L1 expression as a predictive biomarker in metastatic triple-negative breast cancer: exploratory analysis from KEYNOTE-119.

The efficacy of pembrolizumab monotherapy versus chemotherapy increased with increasing programmed death ligand 1 (PD-L1) expression, as quantified by combined positive score (CPS; PD-L1 expression on both tumour cells and immune cells) in patients with previously treated metastatic triple-negative breast cancer (mTNBC) in the phase 3 KEYNOTE-119 study. This exploratory analysis was conducted to determine whether the expression of PD-L1 on tumour cells contributes to the predictive value of PD-L1 CPS in mTNBC. PD-L1 expression in tumour samples was assessed using PD-L1 IHC 22C3 pharmDx and quantified using both CPS and tumour proportion score (TPS; PD-L1 expression on tumour cells alone). Calculated immune cell density (CID) was defined as CPS minus TPS. The ability of each scoring method (CPS, TPS, and CID) to predict clinical outcomes with pembrolizumab was evaluated. With pembrolizumab, the area under the receiver operating characteristic curve was 0.69 (95% CI = 0.58-0.80) for CPS, 0.55 (95% CI = 0.46-0.64) for TPS, and 0.67 (95% CI = 0.56-0.77) for CID. After correction for cutoff prevalence, CPS performed as well as, if not better than, CID with respect to predicting objective response rate, progression-free survival, and overall survival. Data from this exploratory analysis suggest that, although PD-L1 expression on immune cells alone is predictive of response to programmed death 1 blockade in mTNBC, adding tumour PD-L1 expression assessment (i.e. CPS, which combines immune cell and tumour cell PD-L1 expression) may improve prediction. PD-L1 CPS thus remains an effective and broadly applicable uniform scoring system for enriching response to programmed death 1 blockade with pembrolizumab in mTNBC as well as other tumour types.

PD-L1 (CD274) promoter hypomethylation predicts immunotherapy response in metastatic urothelial carcinoma

ABSTRACT PD-L1 status assessed by immunohistochemistry (IHC) has failed to reliably predict outcomes for patients with metastatic urothelial carcinoma (mUC) on immune checkpoint blockade (ICB). PD-L1 promoter methylation is an epigenetic mechanism that has been shown to regulate PD-L1 mRNA expression in various malignancies. The aim of our present study was to evaluate the predictive potential of PD-L1 promoter methylation status (mPD-L1) in ICB-treated mUC compared to conventional IHC-based PD-L1 assessment. We quantified mPD-L1 in formalin-fixed and paraffin-embedded tissue sections using an established quantitative methylation-specific PCR assay (qMSP) in a well-characterized multicenter ICB-treated cohort comprising N = 107 patients with mUC. Additionally, PD-L1 protein expression in tumor tissues was assessed using regulatory approved IHC protocols. The effect of pharmacological hypomethylation by the DNA methyltransferase inhibitor decitabine in combination with interferon-γ stimulation in urothelial carcinoma cell lines was investigated by IHC and FACS. mPD-L1 hypomethylation predicted objective response rate at the first staging on ICB. Patients with tumors categorized as PD-L1 hypomethylated (lower quartile) showed significantly prolonged progression-free (PFS) and overall survival (OS) after ICB initiation. In contrast, PD-L1 protein expression status neither correlated with response nor survival. In multivariable Cox regression analyses, PD-L1 promoter hypermethylation remained an independent predictor of unfavorable PFS and OS. In urothelial carcinoma cell lines, pharmacological demethylation led to an upregulation of membranous PD-L1 expression and an enhanced inducibility of PD-L1 expression by interferon γ. Hypomethylation of the PD-L1 promoter is a promising predictive biomarker for response to ICB in patients with mUC.

Predicting Objective Response of Pembrolizumab in Platinum-Refractory Urothelial Carcinoma Based on Neutrophil-Lymphocyte Ratio Fluctuation and Liver Metastases

Introduction: It is well known that patients with objective response to pembrolizumab have a durable duration of response, leading to favorable survival outcomes. We investigated the possibility of predicting the objective response with concise indicators obtained from daily clinical practice. Methods: In our multi-institutional cohort, 220 platinum-refractory metastatic urothelial carcinoma (mUC) patients treated with pembrolizumab for at least 6 weeks with complete information of objective response were investigated. Results: The median follow-up was 7.3 months, and 119 patients deceased during the follow-up. A multivariate logistic regression analysis exhibited two independent variables predicting the objective response, including the neutrophil-lymphocyte ratio (NLR) change at 6 weeks of treatment and liver metastasis. We proposed a risk group using these two indicators. Patients with no predictive indicators/one of those were assigned to favorable (42%)/intermittent (47%) risk groups. Patients with both indicators were assigned to poor risk (11%). Notably, the objective response rate was well delineated in 41%, 25%, and 0% for favorable-, intermediate-, and poor-risk groups, respectively (p < 0.001). Distinct overall survival (OS) between the risk groups was also confirmed with the median OS of 14.1, 11.7, and 4.2 months in favorable-, intermediate-, and poor-risk groups, respectively. Conclusions: At the 6 weeks of the pembrolizumab treatment, our risk model predicts the objective response rate precisely. Notably, those classified as “poor risk” – marked by liver metastasis and an increased NLR – should be considered for alternative therapy with a different mode of action, highlighting a critical decision point in treatment optimization.

Predictive Partition Dosimetry and Outcomes after Yttrium-90 Resin Microsphere Radioembolization of Colorectal Cancer Metastatic to the Liver: A Retrospective Analysis

PurposeTo characterize estimated absorbed tumor dose (ADT), objective response (OR), and estimated target dose of liver metastatic colorectal cancer (mCRC) after resin microsphere yttrium-90 (90Y) radioembolization using partition dosimetry. Materials and MethodsIn this retrospective, single-center study, multicompartment dosimetry of index tumors undergoing 90Y radioembolization from October 2013 to July 2022 was performed using MIM SurePlan and pretreatment technetium-99m macroaggregated albumin infusion data. Thirty-eight patients with mCRC underwent treatments for 59 index tumors. Patients were imaged every 2–3 months after treatment and then every 3–6 months after disease control to determine the best response per Response Evaluation Criteria in Solid Tumors 1.1. Responses were categorized as OR or nonresponse (NR). A Cox proportional hazards model evaluated the probability of tumor OR and local progression–free survival (LPFS) based on ADT. ResultsPatients had a median follow-up of 116 days (interquartile range [IQR], 69–231 days). The ADT was higher for OR patients than for NR patients (median, 130.8 [IQR, 85.6–239.0] vs 40.6 [IQR, 26.0–66.3] Gy; P < .001). A greater percentage of OR than NR patients were treated with activities calculated by partition modeling (54% vs 12%; P = .005). Only ADT predicted response (P = .032). At 6 months, an ADT of 120 Gy predicted a 55% (95% CI, 0.0%–89%) probability of OR. Only ADT (P = .010) and female sex (P = .014) predicted LPFS. At 1 year, an ADT of 120 Gy predicted a 70% (95% CI, 35%–100%) probability of LPFS. ConclusionsTumor dose was the strongest predictor of OR for mCRC. Administration of an estimated 120 Gy to mCRC predicted 55% OR with 90Y resin microspheres at 6 months.

Оценка параметров иммунного статуса в качестве факторов прогнозирования эффективности терапии в контексте механизмов развития противоопухолевого иммунного ответа

The purpose of the study was to evaluate the prognostic and predictive value of lymphocyte subpopulations estimation in the peripheral blood during systemic therapy in patients with solid tumors. Material and methods. We performed a pooled analysis of a series of phase I–II or IV prospective trials conducted in the N.N. Petrov National Medical Research Center of Oncology since 1998 to 2017. Immune system condition (immune status) was assessed 2932 times in 532 patients. 64.5% of patients had melanoma, 16.5% – kidney cancer, 11.4% – soft tissue sarcoma, 7.6% – primary multiple tumors with inclusion of above mentioned diseases. Stages I–II was diagnosed in 12.1% of cases, stage III in 27.5%, stage IV in 60.5%. We present results of the complex assessment of immune factors as predictors of objective response, time to progression or overall survival. Results. We confirmed predictive role of the anergy for efficacy of anticancer vaccines in melanoma. We found several factors that have change their prognostic and predictive value to the opposite in relation to primary tumor, therapy setting (adjuvant or therapeutic), type of therapy. We have shown the efficacy of chemotherapy and targeted therapy in patients with high CD3+CD4+CD24highCD127low lymphocytes level. We have shown favorable prognostic value of elevated CD3+CD4+CD8+ cells level. We demonstrated relation between immune response evaluation and mechanism of action in different types of vaccines. Features of immunological profiles of predictive factors for different diseases, different types of therapy are presented. Conclusion. Evaluation of the main parts of the immune system and allocation of different syndromes is essential for the prediction do the therapy efficacy based on immune system parameters.

First report of tumor-informed circulating tumor DNA in fibrolamellar carcinoma in adolescents and young adults: 51 patients.

3034 Background: Fibrolamellar Carcinoma (FLC) is an extremely rare form of primary liver cancer (50-200 cases/year), unrelated to hepatocellular carcinoma. The only established standard of care is surgery. However, recurrence is at least 80%, even with R0 surgery, suggesting that FLC is best thought of as a systemic disease instead of a surgical disease. Systemic therapies in retrospective studies of more than 25 show immunotherapy and chemotherapy combinations have promise. Circulating “tumor-informed” tumor DNA (ctDNA) is a tool for tracking microscopic systemic disease, serving as a marker of systemic involvement, has shown success in management of other cancers. FLC has never been studied using this technology. Our retrospective study of 51 patients over a 2.5-year period is the first attempt to understand if ctDNA can be helpful in the management of FLC. Methods: All patients consented to blood draws on a 1-3 monthly basis, according to the company (Signatera) protocol. All patients had regular imaging reviewed by the FibroFighters Foundation National Tumor Board. Complete response (CR), Partial response (PR) (&gt; 30% decrease), Progressive Disease (PD) (&gt; 20% increase), and Stable Disease (SD) (neither PD/PR/CR) were recorded according RECIST 1.1. The imaging closest in time to the blood draw was used for correlation purposes. “Negative” is defined as a value = 0.00. “Positive” is defined as a value &gt; 0.00. Results: Fifty-one FLC patients (26M, 25F, median age 23, range 10-56), 92% stage (III/IV), consented to ctDNA “blood biopsies,” (n=226 samples, mean 4.4/patient, range 1-11). Negative values correlated with no evidence of disease (NED) on imaging 78% of the time, positive values with disease 94% [PPV/NPV/sens/spec of 0.78/0.94/0.93/0.81]. NPV and sens increased to almost 100% with a ctDNA threshold of &gt; 1.0 at the expense of a lower PPV and spec. However, consecutive negative ctDNA values correlated with radiologic NED 86% [PPV/NPV/sens/spec = 0.86/0.84/0.77/0.91] without a single PD in this cohort (n=64). A “downward trend or slope” on consecutive values predicted Objective Response (CR+PR) and Disease Control (CR+PR+SD) at 68% and 96% [PPV/NPV/sens/spec of 0.68/0.49/0.36/0.78 and 0.96/0.26/0.36/0.94, respectively], while an “upward trend” performed less well to predict relapse [PPV/NPV/sens/spec = 0.54/0.98/0.94/0.81]. Conclusions: In this first exploration of 51 young FLC patients, and 226 tissue-informed cell free ctDNA data points, “Positive” and “Negative” values overall performed well for predicting RECIST 1.1 “disease” or “NED” respectively, especially for consecutive values of zero, where no progressions were observed in 64 distinct FLC scenarios. A downward trend or “slope” performed better than an upward trend to predict objective response (PR+CR) versus relapse (PD).

Predictors of response in EGFR-mutant metastatic non-small cell lung cancer patients treated with tyrosine kinase inhibitors.

The goal of the study was to evaluate the efficacy of tyrosine kinase inhibitors in patients with epidermal growth factor receptor (EGFR)-mutant metastatic non-small cell cancer and to determine the factors that predict objective response. In the study, data from metastatic non-small cell lung cancer patients with EGFR mutations treated with tyrosine kinase inhibitors were retrospectively reviewed. Factors predicting objective response were evaluated with logistic regression analysis. The study evaluated the data of 105 patients. The most common EGFR mutations detected in patients were exon 19 (56.2%) and exon 21 (23.8%). The median progression-free survival (PFS) associated with EGFR tyrosine kinase inhibitors was 20.1 (95% confidence interval [CI], 13.4-26.7) months. The median overall survival (OS) in the post-metastasis period was found to be 30.8 (95% CI, 20.2-41.4) months. Five- and seven-year OS was determined as 28.7% and 22.9%, respectively. Factors predicting the objective response were analyzed. Presence of drug-related toxicity (P = 0.02), histopathologic type (P = 0.01), metastasis burden (P = 0.03), and EGFR mutation type (P = 0.04) were found to be statistically significant in multivariate analysis. In our study, we found that EGFR tyrosine kinase inhibitors are effective and safe. Better response to EGFR inhibitors was observed in the presence of drug-induced toxicity, adenocarcinoma histology, low metastasis burden, and exon 19 mutation.

Abstract 2171: A multivariate biomarker predicts sacituzumab govitecan response in solid tumors

Abstract Despite the recent clinical success of antibody drug conjugates (ADC) in oncology, predictive biomarkers are lacking, potentially limiting their impact. Herein, we evaluated the ability of candidate biomarkers alone and in combination to predict objective response rates observed in solid tumor patients treated with the TROP2-targeted ADC, sacituzumab govitecan (SG), as determined in the IMMU-12-01 basket trial. We leveraged available next generation sequencing (NGS)-based molecular profiling data from an independent advanced solid tumor cohort (n = 23,968) and developed a multivariate biomarker algorithm that produced biomarker positive rates correlating with the objective response rates (ORR) observed in IMMU-12-01. Candidate biomarkers evaluated included TROP2 gene expression, proliferation (by gene expression) and tumor cellularity. Notably, while TROP2 gene expression was highly correlated with protein expression across 45 tumor types (r = 0.93), TROP2 gene expression alone did not significantly predict ORR across 9 tumor types (r = 0.40, p = 0.29). In contrast, a biomarker algorithm combining TROP2 and proliferation by gene expression with tumor cellularity strongly predicted response both when using tumor type-specific biomarker rates in a discovery cohort (r = 0.83, p = 0.006) and in an independent validation cohort (r = 0.82, p = 0.007). These results indicate that the multivariate biomarker accounts for 67% of the variability observed in response rates and may thus identify patients likely to benefit from SG. Among tumor types with objective responses in IMMU-12-01, biomarker positive rates ranged from 9.9% in colorectal cancer to 57.4% in bladder cancer. Additional tumor types with biomarker positive rates &amp;gt;30% included cancers of the head and neck, cervix, salivary gland, skin (non-melanoma) and ovary, all with positive biomarker rates &amp;gt;30%. Interestingly, most tumor types had biomarker positive rates &amp;gt;5%, suggesting the potential for a tumor type-agnostic approach to patient selection. Considering SG and other ADC’s mechanism of action, a plausible model for response is that (1) higher target expression increases ADC drug delivery, (2) higher tumor cellularity increases ADC bystander effect and (3) higher tumor cell proliferation increases tumor cell death. In summary, we uncovered a novel biomarker algorithm capable of predicting SG response across solid tumors that may be generalizable to ADCs as a class, with the potential to further optimize use and maximize benefit. Citation Format: Nickolay A. Khazanov, Laura E. Lamb, Daniel H. Hovelson, Kat Kwiatkowski, D. Bryan Johnson, Daniel R. Rhodes, Scott A. Tomlins. A multivariate biomarker predicts sacituzumab govitecan response in solid tumors [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 2171.

Nomogram for tumour response based on prospective cohorts of hepatocellular carcinoma patients receiving immunotherapy combined with targeted therapy: development and validation.

Although immunotherapy combined with targeted therapy can be effective for hepatocellular carcinoma (HCC), not all HCC patients respond to this treatment. Models for predicting tumour response in HCC patients receiving immunotherapy combined with targeted therapy are lacking. A total of 221 HCC patients from two independent prospective cohorts were retrospectively reviewed. The patients were randomly divided into training and validation cohorts at a ratio of 7:3. Standard clinical data were collected from each patient, including age, sex, hepatitis B infection status, laboratory tests, and immune target-related adverse events (itrAEs). Tumour responses were evaluated using the Response Evaluation Criteria in Solid Tumours (RECIST) v1.1 guidelines. ItrAEs were assessed based on the Common Terminology Criteria for Adverse Events version 4.0. The nomogram for tumour response prediction was constructed based on the results of the multivariate logistic regression analysis, areas under the receiver operating characteristic curves (AUROCs) were used to determine the sensitivity and specificity of the model, and calibration plots and Hosmer-Lemeshow chi-square tests were performed to assess the calibration of the model. In the multivariate logistic regression analysis, a solitary tumour (P=0.006), neutropenia (P=0.003) and hypertension (P=0.042) independently predicted objective response (OR). A nomogram for OR was established with AUROCs of 0.734, 0.675, 0.730, and 0.707 in the training, validation, first-line and second-line treatment sets, respectively. Tumour sizes less than 5 cm (P=0.005), a solitary tumour (P=0.037), prognostic nutritional indices greater than or equal to 54.3 (P=0.037), neutropenia (P=0.004) and fatigue (P=0.041) independently predicted disease control (DC). A nomogram for DC was established with AUROCs of 0.804, 0.667, and 0.768 in the training, first-line and second-line treatment sets, respectively. All the Hosmer-Lemeshow tests and calibration curves showed acceptable calibration. The current provides clinicians with new insights into selecting patients for immunotherapy combined with targeted therapy and contributes to the development of immunotherapy for HCC. It is necessary to expand the scale of our research and perform prospective studies to verify our findings.

Effect of genetic polymorphisms on outcomes following nivolumab for advanced renal cell carcinoma in the SNiP-RCC trial.

Anti-PD-1 antibodies are widely used for cancer treatment including advanced renal cell carcinoma (RCC). However, their therapeutic and adverse effects vary among patients. This study aimed to identify genetic markers that predict outcome after nivolumab anti-PD-1 antibody treatment for advanced RCC. This study was registered on the website of the University Hospital Medical Information Network (protocol ID, UMIN000037739). Patient enrollment was conducted at 23 institutions in Japan between August 19, 2019, and September 30, 2020. Patient follow-up ended on March 31, 2021. Patients were treated with nivolumab for advanced clear cell RCC. A genome-wide association study was performed in the development set, while genotyping of target regions in the validation set was undertaken. Single nucleotide polymorphisms (SNPs) in genes of interest CD274, PDCD1LG2 and PDCD1 were genotyped in the combined set. The primary endpoint was the association of SNPs with objective response following nivolumab treatment. As secondary endpoints, the associations of SNPs with radiographic progression-free survival (rPFS) and treatment-related grade ≥ 3 adverse events (AEs) were evaluated. A genome-wide association study followed by a validation study identified that SNPs in FARP1 (rs643896 and rs685736) were associated with objective response and rPFS but not AEs following nivolumab treatment. Furthermore, SNPs in PDCD1LG2 (rs822339 and rs1411262) were associated with objective response, rPFS, and AEs following nivolumab treatment. Genetic risk category determined according to the number of risk alleles in SNPs (rs643896 in FARP1 and rs4527932 in PDCD1LG2) excellently predicted objective response and rPFS in nivolumab treatment. This study revealed that SNPs in FARP1 and PDCD1LG2 were correlated with outcome in nivolumab treatment. The use of these SNPs may be beneficial in selecting appropriate treatment for individual patients and may contribute to personalized medicine.

Tumor Radiomic Features on Pretreatment MRI to Predict Response to Lenvatinib plus an Anti-PD-1 Antibody in Advanced Hepatocellular Carcinoma: A Multicenter Study

Introduction: Lenvatinib plus an anti-PD-1 antibody has shown promising antitumor effects in patients with advanced hepatocellular carcinoma (HCC), but with clinical benefit limited to a subset of patients. We developed and validated a radiomic-based model to predict objective response to this combination therapy in advanced HCC patients. Methods: Patients (N = 170) who received first-line combination therapy with lenvatinib plus an anti-PD-1 antibody were retrospectively enrolled from 9 Chinese centers; 124 and 46 into the training and validation cohorts, respectively. Radiomic features were extracted from pretreatment contrast-enhanced MRI. After feature selection, clinicopathologic, radiomic, and clinicopathologic-radiomic models were built using a neural network. The performance of models, incremental predictive value of radiomic features compared with clinicopathologic features and relationship between radiomic features and survivals were assessed. Results: The clinicopathologic model modestly predicted objective response with an AUC of 0.748 (95% CI: 0.656–0.840) and 0.702 (95% CI: 0.547–0.884) in the training and validation cohorts, respectively. The radiomic model predicted response with an AUC of 0.886 (95% CI: 0.815–0.957) and 0.820 (95% CI: 0.648–0.984), respectively, with good calibration and clinical utility. The incremental predictive value of radiomic features to clinicopathologic features was confirmed with a net reclassification index of 47.9% (p < 0.001) and 41.5% (p = 0.025) in the training and validation cohorts, respectively. Furthermore, radiomic features were associated with overall survival and progression-free survival both in the training and validation cohorts, but modified albumin-bilirubin grade and neutrophil-to-lymphocyte ratio were not. Conclusion: Radiomic features extracted from pretreatment MRI can predict individualized objective response to combination therapy with lenvatinib plus an anti-PD-1 antibody in patients with unresectable or advanced HCC, provide incremental predictive value over clinicopathologic features, and are associated with overall survival and progression-free survival after initiation of this combination regimen.

Analysis of Treatment Outcomes and Prognosis After Concurrent Chemoradiotherapy for Locally Advanced Cervical Cancer.

The aims of this study were to investigate the short-term and long-term efficacies and chronic radiotoxicity of concurrent chemoradiotherapy (CCRT) combined with image-guided adaptive brachytherapy (IGABT) in patients with locally advanced cervical cancer (LACC) and identify prognostic factors in this patient population. The clinical data of 204 patients with cervical cancer who completed CCRT and subsequent brachytherapy in our hospital between February 2015 and March 2017 were retrospectively analyzed. Short-term and long-term outcomes, chronic radiotoxicity, and prognostic factors were assessed. The median follow-up was 61.1 months. The short-term objective response (OR) rate was 85%. Lymph node metastasis before treatment was an independent predictor of OR (HR = 6.290, 95% CI: 2.211-17.897, p = 0.001). Fifty-two patients developed recurrence, with a median recurrence-free survival of 9.9 months (range, 2.4-52.2 months) and a post-recurrence survival of 12.1 months (range, 2.9-58.1 months). At 3 years, the cumulative incidence of overall recurrence was 26% (95% CI: 17-36). Multivariate analysis showed that Stage IIIB (HR = 2.332, 95% CI: 1.195-4.551, p = 0.013; reference, Stage IIB) and lymph node metastasis (HR = 4.462, 95% CI: 2.365-8.419, p < 0.001) were significant independent predictors of recurrence. Fifty-three patients developed chronic radiation proctitis (CRP). The incidence of severe CRP was approximately 5%, and the average rectal accumulation in patients with severe CRP was 73.4 Gy which is 3.9 Gy higher than that in patients without CRP (p = 0.013). At 4 years, the overall survival (OS) and disease-free survival rates were 65% and 62%, respectively, and lymph node metastasis before treatment was an independent prognostic risk factor for OS. The short-term and long-term efficacies of CCRT combined with IGABT for the treatment of LACC patients were relatively satisfactory. However, the short-term and long-term efficacies of patients with lymph node metastasis before treatment were poor. For patients with lymph node metastasis before treatment, more active individualized treatment strategies should be adopted. When designing a radiotherapy plan, it is necessary to strictly limit the rectal accumulation to prevent serious CRP.

Density patterns of tumor-infiltrating lymphocytes and association with objective response to nivolumab in patients with lung adenocarcinoma from CheckMate 057.

2662 Background: Immune checkpoint inhibitors (ICIs) are currently approved for use as therapy in advanced stage lung adenocarcinoma (LUAD). ICIs can decrease risk of progression by up to 60% when compared to chemotherapy, but only about 20% of patients (pts) show response. Given that high levels of tumor-infiltrating lymphocytes (TILs) have been shown to be associated with better prognosis, here, we assess whether computationally derived TIL density measures on digitized H&amp;E images can predict RECIST derived response to nivolumab in LUAD in Checkmate 057 (CM057). CM057 is a clinical trial designed to compare the overall survival of metastatic non-squamous non-small cell lung cancer subjects treated with either nivolumab or docetaxel after failing platinum-based chemotherapy. Methods: H&amp;E-stained samples of 683 LUAD pts were collected from TCGA (n = 421), University of Bern (UBern) (n = 100), and CM057 (n = 162). Tumor response was assessed using RECIST v1.1. Samples were digitized as whole slide images. 294 pts randomly selected from TCGA formed the training set. The remaining 389 pts were used for validation in response to nivolumab in CM057 and prognosticating overall survival (OS) in UBern and TCGA. Computerized algorithms automatically identified TILs and extracted features related to quantity and compactness of TILs with respect to other surrounding nuclei. The top 6 features, determined by least absolute shrinkage and selection operator, were used to train Cox regression models that assign a death risk score to each patient. Pts with risk scores higher than the training median score were considered “high risk” or “non-responders” while pts with lower scores were considered “low risk” or “responders”. Results: The classifier predicted objective response in CM057 with an AUC = 0.61. Additionally, survival analysis showed that the model was prognostic for OS with hazard ratios of 2.38 (confidence interval (CI): 1.32-4.29, p = 0.01, n = 127) in TCGA and 2.37 (CI: 1.32-4.25, p &lt; 0.01, n = 100) in UBern. Conclusions: A computerized image analysis model based on measurements of TIL density showed association with response to treatment in LUAD pts who received nivolumab and was prognostic of OS. Although the AUC was not high, the results suggest analysis of TILs has potential to identify pts who will respond to treatment. Future work will include training a classifier using response to treatment as endpoint and combining the TIL measures with other biomarkers like TMB or PD-L1.

Radiomics Analysis on Gadoxetate Disodium-Enhanced MRI Predicts Response to Transarterial Embolization in Patients with HCC.

Objectives: To explore the potential of radiomics on gadoxetate disodium-enhanced MRI for predicting hepatocellular carcinoma (HCC) response after transarterial embolization (TAE). Methods: This retrospective study included cirrhotic patients treated with TAE for unifocal HCC naïve to treatments. Each patient underwent gadoxetate disodium-enhanced MRI. Radiomics analysis was performed by segmenting the lesions on portal venous (PVP), 3-min transitional, and 20-min hepatobiliary (HBP) phases. Clinical data, laboratory variables, and qualitative features based on LI-RADSv2018 were assessed. Reference standard was based on mRECIST response criteria. Two different radiomics models were constructed, a statistical model based on logistic regression with elastic net penalty (model 1) and a computational model based on a hybrid descriptive-inferential feature extraction method (model 2). Areas under the ROC curves (AUC) were calculated. Results: The final population included 51 patients with HCC (median size 20 mm). Complete and objective responses were obtained in 14 (27.4%) and 29 (56.9%) patients, respectively. Model 1 showed the highest performance on PVP for predicting objective response with an AUC of 0.733, sensitivity of 100%, and specificity of 40.0% in the test set. Model 2 demonstrated similar performances on PVP and HBP for predicting objective response, with an AUC of 0.791, sensitivity of 71.3%, specificity of 61.7% on PVP, and AUC of 0.790, sensitivity of 58.8%, and specificity of 90.1% on HBP. Conclusions: Radiomics models based on gadoxetate disodium-enhanced MRI can achieve good performance for predicting response of HCCs treated with TAE.