First-line Treatment Selection Research Articles

Genetic Diversity and Drug Resistance Mutations in HIV-1 pol Gene Sequences in the Philippines: A Retrospective Genomic Analysis

The Philippines has experienced a significant increase in HIV-1 infections in recent years, with a growing epidemic driven by the CRF01_AE strain. Understanding the genetic diversity of HIV-1 in the Philippines is crucial for the development of effective treatment strategies and the prevention of drug resistance. This study analyzed comprehensive data on common resistance mutation patterns from 2009 to 2017, revealing an increasing trend of mutations observed in NRTI and NNRTI resistance among the predominant CRF01_AE strains. The most common NRTI mutations observed were M184V, K65R, and S68G, whereas the most common NNRTI mutations were K103N, Y181C, and G190A. The study also found a high prevalence of M184V minority variants (0.5-20%) in treatment-naive patients, which could increase the risk of virological failure in 3TC-containing regimens. The findings of this study highlight the importance of comprehensive drug resistance surveillance and access to resistance testing to guide optimal first-line antiretroviral treatment selection and to manage the growing HIV-1 epidemic in the Philippines. The development of effective strategies to prevent and manage drug resistance is crucial to ensuring the long-term success of HIV treatment programs in the country.

First-line Treatment Selection for Advanced Hepatocellular Carcinoma

The treatment landscape of advanced/unresectable hepatocellular carcinoma (uHCC) has rapidly evolved since 2018. Over recent years, various systemic therapies and treatment approaches have been explored. Systemic therapy has primarily relied on tyrosine kinase inhibitors (TKIs); however, immune checkpoint inhibitors (ICIs) have more recently entered the realm of the treatment armamentarium.

Current status of palliative care for patients with unresectable metastatic colorectal cancer in China: a questionnaire-based survey

Objective: To analyze the current adoption of palliative care by patients with unresectable metastatic colorectal cancer (mCRC) in China. Methods: From 1 March 2023 to 30 June 2023, a questionnaire survey was conducted by random sampling. An exclusive research platform for the Blue Book on Clinical Diagnosis and Treatment of Metastatic Colorectal Cancer. An online questionnaire was sent to medical oncologists (including chief physicians, associate chief physicians, attending physicians and residents) in general hospitals and oncology hospitals in four major regions of East, Central, South and Northeast China. The questionnaire contained 28 questions requesting basic information about doctors, the number of patients with mCRC, the status of treatment from first to fourth line and beyond, points concerning treatment of pain in patients with mCRC, and expectations for the future. A medical team was responsible for the quality control of data collected, whereas statisticians performed the data cleaning and sorting and statistical analysis. Results: A total of 300 clinical questionnaires were collected, including 217 (72%) from doctors in general hospitals and 83 (28%) from doctors in oncology hospitals. Senior physicians (including associate chief physicians and chief physicians) accounted for 65% of the respondents, attending physicians 30%, and residents 5%. Within 3 months (average for each month), 46.4±26.6% patients were diagnosed with recurrent or unresectable mCRC by each physician, 51.6±26.8% of the patients being in cancer hospitals and 44.4±26.3% in general hospitals. One hundred percent of patients receiving first-line treatment received palliative care, as did 80.3% of those receiving second-line treatment, 58.2% of those receiving third-line treatment, and 35.1% of those receiving ≥fourth-line treatment. The primary factor governing selection of first-line treatment was guideline recommendations, whereas comorbidities and the patients' physical status dictated second line to fourth line treatment. Standard first-line treatment was administered to 93.8% of eligible patients, standard second-line treatment to 94.3%; and standard third-line treatment to 73.5%. First-line therapy included targeted therapy in 63.6% of patients and immunotherapy in 2.8%; second-line therapy included targeted therapy in 63.0% of patients and immunotherapy in 2.0%; third-line therapy included targeted therapy in 59.2% of patients and immunotherapy in 2.2%; and fourth-line therapy included targeted therapy in 48.7% of patients and immunotherapy in 3.1%. First-line treatment lasted an average of 9.6 months, second-line treatment 6.7 months, third-line treatment 4.9 months, and fourth-line treatment 3.7 months. More than 70% of the patients maintained a good quality of life after receiving first and second-line treatment and more than 60% of them had ECOG performance scores of 0-1. After receiving third- and fourth-line treatment, 50%-60% of patients maintained a good quality of life and 40%-50% of them maintained ECOG performance scores of 0-1. The survey also revealed that the main deficiencies in treatment were limited effectiveness of third-line treatment, insufficient availability and opportunity for clinical research, popularity of new drugs or new drug combination strategies, and limited channels for participation in multidisciplinary diagnosis and treatment. Clinicians reported looking forward to participating in more clinical research on new drugs, hearing about the experience of experts in the field, and discovery of new targets and new drugs that increased the options for posterior line treatment of colorectal cancer. Conclusions: This report objectively summarizes the current situation, treatment difficulties, and expectations of frontline physicians concerning management of mCRC, thus providing a basis for decision-making and future direction for the diagnosis and research on treatment of mCRC.

Impact of PROphet Test in Changing Physicians' Therapeutic Decision-Making for Checkpoint Immunotherapy in Non–Small-Cell Lung Cancer

PurposeImmune Checkpoint Inhibitor (ICI) regimens are approved for first-line treatment of metastatic nononcogene-driven NSCLC. Guidelines do not differentiate which patients with PD-L1 ≥ 50% should receive ICI monotherapy. The clinically validated PROphet NSCLC plasma proteomic-based test is designed to inform this therapeutic decision. MethodsOne hundred oncologists were presented with 3 “virtual” metastatic NSCLC cases with PD-L1 scores and asked to recommend an approved first-line regimen. They then watched an online educational webinar on the PROphetNSCLC test. Postwebinar, the same cases were represented with the addition of a PROphet result, and oncologists again recommended a first-line regimen. Responses were compared to assess the impact on first-line treatment selection. ResultsTreatment recommendation changed in 39.6% of PROphet-tested cases, with 93% of physicians changing at least 1 case. In the PD-L1 ≥ 50% group, 89% of physicians changed their recommendation, followed by 77%, in PD-L1 < 1%, and 36% in PD-L1 1% to 49%. ​In the PD-L1 ≥ 50%, PROphet POSITIVE group, the recommendation for ICI monotherapy increased from 60% to 89%. ​For the PD-L1 ≥ 50%, PROphet NEGATIVE group, the recommendation for monotherapy dropped from 60% to 9%. In the PD-L1 < 1%, PROphet NEGATIVE group, 35% of patients were spared toxicity from ICI compared to 11% in PROphet untested cases. ConclusionAdding PROphet to PD-L1 expression impacted therapeutic decision making in first-line NSCLC. PROphet identifies those predicted to have an overall survival benefit from ICI monotherapy versus combination versus chemotherapy, improving the probability of efficacy and reducing toxicity for some patients.

Survival by first-line therapy and prognostic group among men with metastatic castration-resistant prostate cancer.

Metastatic castration-resistant prostate cancer (mCRPC) is a heterogeneous disease with prognoses varying from months to years at time of castration-resistant diagnosis. Optimal first-line therapy for those with different prognoses is unknown. We conducted a retrospective cohort study of men in a national healthcare delivery system receiving first-line therapy for mCRPC (abiraterone, enzalutamide, docetaxel, or ketoconazole) from 2010 to 2017, with follow-up through 2019. Using commonly drawn prognostic labs at start of mCRPC therapy (hemoglobin, albumin, and alkaline phosphatase), we categorized men into favorable, intermediate, or poor prognostic groups depending on whether they had none, one to two, or all three laboratory values worse than designated laboratory cutoffs. We used Kaplan-Meier methods to examine prostate specific antigen (PSA) progression-free and overall survival (OS) according to prognostic group and first-line therapy, and multivariable cox regression to determine variables associated with survival outcomes. Among 4135 patients, median PSA progression-free survival (PFS) was 6.9 months (95% confidence interval [CI] 6.6-7.3), and median OS 18.8 months (95% CI 18.0-19.6), ranging from 5.7 months (95% CI 4.8-7.0) in the poor prognosis group to 31.3 months (95% CI 29.7-32.9) in the favorable group. OS was similar regardless of initial treatment received for favorable and intermediate groups, but worse for those in the poor prognostic group who received ketoconazole (adjusted hazard ratio 2.07, 95% CI 1.2-3.6). PSA PFS was worse for those who received ketoconazole compared to abiraterone across all prognostic groups (favorable HR 1.76, 95% CI 1.34-2.31; intermediate HR 1.78, 95% CI 1.41-2.25; poor HR 8.01, 95% CI 2.93-21.9). Commonly drawn labs at mCRPC treatment start may aid in predicting survival and response to therapies, potentially informing discussions with care teams. First-line treatment selection impacts disease progression for all men with mCRPC regardless of prognostic group, but impacted OS only for men with poor prognosis at treatment start.

Abstract PO2-16-10: Characterising HER2-low status in metastatic breast cancer: a real-world retrospective study of patients at a metropolitan cancer centre in Australia

Abstract Background: Traditionally, breast cancer subtyping has described patients as HER2-negative or HER2-positive, with the latter being those in whom HER2-directed therapies are utilised. A proportion of HER2-negative cancers can be classified as “HER2-low” as they express IHC 1+ or 2+ and are non-amplified on in-situ hybridisation (ISH). Until recently, our widely available HER2-targeted therapies have proven to be ineffective in this subtype. The DESTINY-BREAST04 trial (Modi et al, NEJM 2022) showed progression-free and overall survival efficacy of HER2-directed therapy using the antibody-drug conjugate (ADC) trastuzumab deruxtecan (T-DXd) in HER2-low disease, suggesting a need to identify this population for selected therapy. This study evaluated a real-world population of metastatic breast cancer (MBC) patients treated according to traditional breast cancer subtypes to determine the frequency of HER2-low histology, with a focus on selection of first-line therapy. Methods: This retrospective audit identified patients being treated for MBC at a single tertiary hospital (Austin Health in Melbourne, Australia) over a 3.5 year period. Patients were identified from an electronic database, with a date of last follow-up as August 2022. Medical records were interrogated for clinicopathological information including review of pathology reports for breast cancer subtypes. Traditional breast cancer subtypes were defined as (i) ER positive and HER2-negative (HER2 0 or 1+, or 2+ and non-amplified); (ii) HER2-positive (HER2 3+ and/or amplified) and (iii) TNBC (negative for ER and PgR, and HER2 0 or 1+, or 2+ and non-amplified). Selection and duration of first-line treatment for MBC was also recorded. Results: Between March 2018 and August 2022, 202 MBC patients were identified. Based on traditional subtypes, 126 (62%) were ER positive and HER2-negative, 46 (23%) were HER2-positive, 26 (13%) were TNBC and 4 (2%) were unknown. In the total population, 87 (43%) were classified as having HER2-low disease based on retrospective pathology reports. HER2-low represented 61% of ER positive, HER2-non amplified patients and 45% of TNBC patients. Metastatic specimen data was known for 172 cases and of these, 95 patients (55%) were re-biopsied on metastatic progression. This occurred more commonly with HER2-positive (81%) and ER positive HER2-negative (78%) primaries. The most common change in subtype was loss of ER, which occurred in 7 patients (3%). Changes in HER2 status occurred in 29 (14%) of patients, most commonly as either an increase from 1+ to 2+ or decrease from 1+ to 0. HER2-low status remained consistent for 67% of patients who underwent biopsy on recurrence. For patients with ER positive HER2-negative MBC, first-line combination therapy with a CDK4/6 inhibitor + AI was preferred (51%), followed by single agent endocrine therapy (30%) and chemotherapy (17%). The majority (84%) of HER2-positive patients received first-line dual HER2 therapy (trastuzumab and pertuzumab) with taxane chemotherapy. Half of TNBC patients received upfront chemotherapy, with physician preference being capecitabine (45%). There was no significant difference in PFS between the HER2-low vs. HER2-0 cohorts, for either ER positive or TNBC subtypes. No patients in this series were treated based on HER2-low definition due to no funded access to such therapies at the time. Conclusion: HER2-low has emerged as an identifier for patients who may respond to HER2-directed therapies using ADCs such as T-DXd. However, its clinical significance as a discrete clinical subtype remains uncertain. In this retrospective audit, HER2-low patients were identified in both ER positive and TNBC subtypes. Dynamic changes in HER2 staining occurred infrequently. Further evaluation is needed to address whether HER2-low expression provides any prognostic value and whether prospective reporting of HER2-low status may alter how these new HER2-directed therapies are used in future. Citation Format: Michelle Li, Belinda Yeo. Characterising HER2-low status in metastatic breast cancer: a real-world retrospective study of patients at a metropolitan cancer centre in Australia [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 PO2-16-10.

Multicenter validation study of a treatment selection MAP for pancreatic neuroendocrine tumors.

Somatostatin analogs, molecular-targeted agents and cytotoxic anticancer agents are available as therapeutic agents for the systemic treatment of pancreatic neuroendocrine tumors, and we have developed a first-line treatment selection MAP to enable selection of the optimal treatment strategy for pancreatic neuroendocrine tumors. The purpose of this study was to validate the usefulness of the treatment selection MAP. Patients who had received systemic therapy for a pancreatic neuroendocrine tumor between January 2017 and December 2020 were compared according to whether they had been treated as recommended by the MAP (matched patients) or not (unmatched patients) to determine whether better outcomes were achieved by the matched patients. The primary endpoint was progression-free survival of the matched group and unmatched groups in the somatostatin analog, molecular-targeted agent and cytotoxic anticancer agents areas of the MAP. There were 41 (55%) MAP-matched patients in all areas among the 74 patients registered at seven hospitals. The MAP-matched rates were 100, 77 and 38% in the somatostatin analog area, molecular-targeted agent area and cytotoxic anticancer agents area, respectively. All of the unmatched patients had been selected for less intensive treatment. The median progression-free survival in the matched group and unmatched group in the molecular-targeted agent area of the MAP were 46.6 and 15.4months, respectively, and a multivariate analysis identified MAP-matched (hazard ratio 0.18 [95% confidence interval: 0.04-0.87], P=0.032) as the only significant independent favorable predictive factor. The usefulness of the MAP for treatment selection was validated in the molecular-targeted agent area of the MAP.

Role of patient characteristics in adherence to first-line treatment guidelines in breast, lung and prostate cancer: insights from the Nordic healthcare system

ObjectivesThis study investigates the influence of socioeconomic status, health literacy, and numeracy on treatment decisions and the occurrence of adverse events in patients with breast, lung, and prostate cancer within...

Plasma Proteome-Based Test for First-Line Treatment Selection in Metastatic Non-Small Cell Lung Cancer.

Current guidelines for the management of metastatic non-small cell lung cancer (NSCLC) without driver mutations recommend checkpoint immunotherapy with PD-1/PD-L1 inhibitors, either alone or in combination with chemotherapy. This approach fails to account for individual patient variability and host immune factors and often results in less-than-ideal outcomes. To address the limitations of the current guidelines, we developed and subsequently blindly validated a machine learning algorithm using pretreatment plasma proteomic profiles for personalized treatment decisions. We conducted a multicenter observational trial (ClinicalTrials.gov identifier: NCT04056247) of patients undergoing PD-1/PD-L1 inhibitor-based therapy (n = 540) and an additional patient cohort receiving chemotherapy (n = 85) who consented to pretreatment plasma and clinical data collection. Plasma proteome profiling was performed using SomaScan Assay v4.1. Our test demonstrates a strong association between model output and clinical benefit (CB) from PD-1/PD-L1 inhibitor-based treatments, evidenced by high concordance between predicted and observed CB (R2 = 0.98, P < .001). The test categorizes patients as either PROphet-positive or PROphet-negative and further stratifies patient outcomes beyond PD-L1 expression levels. The test successfully differentiates between PROphet-negative patients exhibiting high tumor PD-L1 levels (≥50%) who have enhanced overall survival when treated with a combination of immunotherapy and chemotherapy compared with immunotherapy alone (hazard ratio [HR], 0.23 [95% CI, 0.1 to 0.51], P = .0003). By contrast, PROphet-positive patients show comparable outcomes when treated with immunotherapy alone or in combination with chemotherapy (HR, 0.78 [95% CI, 0.42 to 1.44], P = .424). Plasma proteome-based testing of individual patients, in combination with standard PD-L1 testing, distinguishes patient subsets with distinct differences in outcomes from PD-1/PD-L1 inhibitor-based therapies. These data suggest that this approach can improve the precision of first-line treatment for metastatic NSCLC.

How We Treat Metastatic Castration-Sensitive Prostate Cancer.

The treatment of metastatic castration-sensitive prostate cancer (mCSPC) has seen remarkable breakthroughs over the last few years. Diagnostic and therapeutic advances have given rise to debates about risk stratification and optimal first-line treatment selection, as well as to concerns about potential overtreatment in a disease state with a highly heterogeneous clinical behavior. Here, we use case reports from our practice to review the clinical trials exploring intensified triplet regimens combining androgen deprivation therapy with second-generation androgen receptor signaling inhibitors and docetaxel, and we offer our recommendations on how to best select candidates for these novel combinations. Furthermore, the growing adoption of PET imaging with increasingly sensitive and prostate tissue-specific tracers replacing conventional staging technologies has led to the identification of a subset of low-volume mCSPC with nodal metastases which would otherwise not be considered abnormal by RECIST criteria. We describe our PSA-adapted approach to treatment in this unique population with non-measurable low-volume mCSPC which has not been specifically investigated in any phase III clinical trials. We also discuss ongoing clinical trials evaluating treatment de-escalation strategies. Finally, we review how local treatment modalities directed at the prostate or distant sites of disease in oligometastatic CSPC may benefit patients, and how we incorporate metastasis-directed therapy in the management of mCSPC.

An Ensemble Machine Learning Model Using Gradient Boosting Identifies Patients with Disease Progression in Newly Diagnosed Multiple Myeloma

Introduction: In many cases, treatment decisions for multiple myeloma patients must be made in the absence of high quality randomized controlled clinical trials. As a result, many clinicians lean on prognostic models to drive treatment selection. The most widely used of these models incorporate a limited number of patient related factors that reflect tumor burden, cytogenetic features, or gene expression profiling. These prediction scores are derived from regression models that incorporate these variables at the time of diagnosis. They do not account for variables that change dynamically over time, do not explore the relationship between treatment selection and progression, and imperfectly predict survival outcomes. We designed a prognostic model based on an ensemble machine learning platform to predict progression in NDMM using Extreme Gradient Boosting Machine (XGBoost) combined with accelerated failure time modeling for survival analysis. Methods: We utilized a large retrospective data set containing treatment and response information for 1127 patients with newly diagnosed multiple myeloma (NDMM) treated at the Cleveland Clinic Foundation between 2000 and 2023. Following data preprocessing based on data completeness, 953 patient records were included in our training, testing, and cross validation datasets. For the purposes of our analysis, the initial dataset was randomly split into training (70%), testing (15%) and validation (15%) subsets at the patient level. We also defined the lower and upper bounds for each subset, which is critical due to the right-censored nature of survival data. Hyperparameters for XGBoost were optimized using Bayesian search, minimizing the negative log-likelihood. A random forest method was applied for the imputation of missing data (MissForest). We then trained the XGBoost model using GPU acceleration for enhanced computational efficiency. Finally, a log-rank test on predicted survival times was used to test the model's performance on patients with and without known progression. Results: Our preprocessed data set had 953 patients with a mean age at disease onset of 65 years and a slight male predominance (55%). Approximately 27% of patients harbored high cytogenetic risk disease and 3.5% of the cohort presented with extramedullary disease a diagnosis. At a median follow up of 35 months, 47% of patients had experienced disease progression or death. Induction therapy included an immunomodulatory drug in 40% of patients and proteasome inhibitors in 22%. Frontline autologous stem cell transplantation followed induction in 28% of patients. Median progression free survival in the overall data set was 44 months. These features were consistent across the randomly assigned training, testing, and validation cohorts. Following data preprocessing, 34 independent clinical and genomic variables including selection of first line treatment were assessed as model inputs each available record. Under optimized parameters, the model was trained with a maximum tree depth = 7 and a learning rate ~ 0.22 for the desired output of progression free survival (PFS). We then queried PFS for both the training and validation sets and divided patients into those known to have progressed and those without a progression event. Survival analysis was undertaken using predicted PFS values for patients known to have progressed and those who remained progression free. For the validation data set, our model successfully discerned between progressed and non-progressed cases (log-rank test statistic = 22.07, p &amp;lt; 0.005) (Figure 1). Discussion: We present a machine learning approach based on regularized gradient boosting that accurately discerns between patients who experience progression and those who remain progression free at a median ~35 months of follow-up in a large retrospective data set in patients presenting with NDMM. Further elaboration of our model will allow for the incorporation of large amounts data to predict survival outcomes on the basis of dynamic variables such as depth of response and treatment selection. With the ability to parse outcomes among multiple myeloma patients at high resolution, future clinicians and clinical trialists may be able to overcome limitations in trial design and patient-level therapy selection for both newly diagnosed and relapsed patients.

ML-based sequential analysis to assist selection between VMP and RD for newly diagnosed multiple myeloma

Optimal first-line treatment that enables deeper and longer remission is crucially important for newly diagnosed multiple myeloma (NDMM). In this study, we developed the machine learning (ML) models predicting overall survival (OS) or response of the transplant-ineligible NDMM patients when treated by one of the two regimens—bortezomib plus melphalan plus prednisone (VMP) or lenalidomide plus dexamethasone (RD). Demographic and clinical characteristics obtained during diagnosis were used to train the ML models, which enabled treatment-specific risk stratification. Survival was superior when the patients were treated with the regimen to which they were low risk. The largest difference in OS was observed in the VMP-low risk & RD-high risk group, who recorded a hazard ratio of 0.15 (95% CI: 0.04–0.55) when treated with VMP vs. RD regimen. Retrospective analysis showed that the use of the ML models might have helped to improve the survival and/or response of up to 202 (39%) patients among the entire cohort (N = 514). In this manner, we believe that the ML models trained on clinical data available at diagnosis can assist the individualized selection of optimal first-line treatment for transplant-ineligible NDMM patients.

Abstract P4-07-51: Impact of the Theralink CLIA protein/phosphoprotein assay on treatment selection in routine clinical practice: a prospective observational study in advanced breast cancer

Abstract Background: The Theralink Reverse Phase Protein Array-based (RPPA) CLIA assay is a novel molecular assay developed to assist physicians in therapy selection for patients diagnosed with advanced/recurrent breast cancer. The assay was specifically designed to quantitatively measure protein activation/phosphorylation of 32 FDA-approved and Phase III drug targets and pathway-linked downstream substrates, providing functional information on actionable oncogenic drivers in individual tumors. Given the recent commercialization of the Theralink assay (TLA), knowledge gaps exist regarding its use in routine clinical practice and the impact of the test on clinical decision-making in real world practice. The primary objective of this analysis was to assess how the TLA has been integrated in therapy selection for breast cancer patients with a focus on understanding its target population, the rationale for patient selection by the treating physician, and the utilization of the molecule information generated by the assay as part of the therapeutic decision-making process. Findings from this study will help optimize patient selection and maximize the clinical impact of the test as a tool for advancing precision oncology. Methods: We prospectively collected data from 124 women with advanced breast cancer whose tumors were profiled using the Breast Cancer TLA from February, 2021 to May, 2022. All patients were confirmed as actively managed for advanced/recurrent breast cancer. Eight μm FFPE sections (n=5) from a recently collected tissue biopsy were used to isolate tumor epithelia via Laser Capture Microdissection and to generate RPPA-based molecular profiles. Clinical management and therapy selection information for 68 patients was gathered via surveys completed by treating physicians. Results: Median age of the 124 participants was 53 years (range 26-82) and 111 (89.5%) patients had stage IV disease. The cohort included 66 hormone receptors positive (of which 5 were HER2+), 51 triple negative, and 7 HR-/HER2+ breast cancers. The TLA yielded molecular information for all specimens and profiles were generated on average in 11 days. One or more targets were highly activated (highly actionable) in 91 patients (73.4%) and moderately activated (partially actionable) in 29 patents; only 4 patients had no actionable target (3.2%). Previous treatment information was available for 118 (95.2%) patients. The TLA was requested to assist with the selection of first-line treatment in 36 patients (of which 24 had previously received neoadjuvant treatment), second-line treatment in 35 patients and third- or subsequential-lines of treatment in 47 patients. Clinicians provided feedback on the use of the assay for 68 patients included in the study. The survey revealed that the TLA impacted treatment selection in 50 (73.5%) cases and was used to either expand options beyond standard of care (30 cases), refine/re-prioritize available treatments (11 cases), narrow treatment from a plethora of options (2 cases) or a combination of the three (7 cases). When TLA data were used for treatment selection, physicians defined the assay highly and moderately beneficial for patient management in 15 and 27 cases, respectively. Conclusion: Our study suggests the TLA yields useful information for selecting treatment for breast cancer patients with advanced/recurrent disease. In this prospective analysis, the assay identified actionable targets in more than 90% of patients, which is significantly higher than what has previously been reported for genomic profiling alone. Overall, physicians found the information yielded by the assay useful for selecting treatment. The inclusion of the TLA in oncology may offer important insights for advancing precision medicine for breast cancer patients. Citation Format: Kris Weinberg, Mariaelena Pierobon, Edik Blais, Justin Davis, Joyce O’Shaughnessy, Emanuel F. Petricoin. Impact of the Theralink CLIA protein/phosphoprotein assay on treatment selection in routine clinical practice: a prospective observational study in advanced breast cancer. [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P4-07-51.

Development of artificial intelligence–derived histological biomarkers for first-line treatment selection in metastatic pancreatic ductal adenocarcinoma (mPDAC).

743 Background: The prognosis of metastatic pancreatic ductal adenocarcinoma (mPDAC) remains poor with a median survival time of 10-12 months. First-line treatment is largely influenced by performance status with fit patients more often receiving FOLFIRINOX (FFX) than Gemcitabine+Nab-Paclitaxel (GNP). Although the two regimens have improved outcomes over gemcitabine monotherapy, no biomarkers routinely used in clinical practice can predict which regimen is optimal to facilitate a precision medicine approach. We developed two signatures (V-FFX and V-GNP) associated with treatment outcomes for the respective first-line regimens using a retrospective cohort of mPDAC cases. Methods: We conducted a retrospective study of mPDAC patients treated at two institutions (UPMC and Cedars Sinai) from 2014 to 2021. Digitized histological H&amp;E sections corresponding to 145 metastatic PDAC patients treated with either first-line FFX or GNP were included. Independent randomized training and test datasets were constructed for FFX-treated (train: 41, test: 25) and GNP-treated (train: 49, test: 30) patients. To construct the histological assay, a deep-learning algorithm then segmented nuclei to extract quantitative histological features. Features associated with disease-specific survival (DSS) for FFX and GNP were identified utilizing univariate Cox proportional hazards (CPH) models for the respective training sets and V-FFX and V-GNP signatures were constructed. DSS stratification of the V-FFX and V-GNP signatures were examined using Kaplan-Meier analysis and the log-rank test and DSS percentages at 12 months were calculated on the respective test sets. Results: The V-FFX and V-GNP signatures were found to be significantly associated with treatment outcomes stratified in the respective test sets (log-rank test, V-FFX: p=0.046, V-GNP: p=0.004). 29 of 55 patients tested positive for only one of either V-FFX and V-GNP signatures. Kaplan-Meier analysis demonstrated robust separation with hazard ratios for the V-FFX and V-GNP signatures of 3.01 (95% CI: 0.96, 9.45) and 4.81 (95% CI: 1.74, 13.3). DSS at 12 months for patients in V-FFX +ve vs -ve groups were 88% (8/9) vs 50% (7/14). DSS at 12 months for patients in V-GNP +ve vs -ve groups were 66% (8/12) vs 15% (2/13). Conclusions: AI derived V-FFX and V-GNP morphological signatures were strongly associated with treatment outcomes for first-line FFX and GNP and can potentially aid in the selection of first-line treatment for mPDAC patients.

Abstract A002: Purity Independent Subtyping of Tumor (PurIST): Real-world data validation of a pancreatic ductal adenocarcinoma (PDAC) gene expression classifier and its prognostic implications

Abstract Background: PDAC is a highly morbid disease with no validated biomarkers for first-line (1L) treatment selection. The PurIST single-sample molecular subtyping gene signature, initially described by Rashid et al. (Clin. Cancer Res., 2020), classifies PDAC tumors as basal or classical. Prior work showed that these subtypes are associated with prognosis and that basal subtype patients have significantly lower objective response rate to FOLFIRINOX (FFX) compared to classical subtype patients. This suggests potential implications for treatment with FFX versus gemcitabine and nab-paclitaxel (GnP). Here, we retrospectively demonstrate the clinical validity of PurIST implemented as a laboratory developed test (LDT) on the Tempus Labs sequencing platform using a real-world dataset of advanced PDAC patients. Methods: De-identified PDAC patients were selected from the Tempus Oncology Data Ecosystem under an IRB-approved protocol according to the following inclusion criteria: no systemic treatment, surgically unresectable/metastatic, available RNA-sequencing data from primary or metastatic tissue, and FFX or GnP as 1L systemic therapy with available outcomes. Sequencing was performed by the CAP/CLIA validated Tempus xT assay. The PurIST model was applied to normalized RNA-sequence abundance files to compute a basal or classical subtype label. Predefined statistical analysis parameters included 12-month survival rate and median overall survival (OS) in FFX treated patients. OS was compared using Kaplan-Meier estimates, hazard ratios, and log-rank statistical tests. Results: 258 PDAC patients (64.9 +/- 9.9 yrs, 42% female), were identified for analysis with median OS of 11.7 months (95% CI: 10.6-13.7) and 12-month censorship rate of 12.8%. 151 patients were treated with FFX and 107 with GnP. 42 patients (28%) receiving FFX and 31 patients (29%) receiving GnP were classified as basal. Among FFX treated patients, median OS was 14.4 months (95% CI: 12.5-16.9) in classical patients vs. 9.4 months (95% CI: 8.3-14.5) in basal patients (HR = 1.72, 95% CI = 1.2-2.6, p=0.006). The 12-month survival rate was significantly lower in basal patients receiving FFX vs. classical patients (33.3% vs. 59.4%, p=0.011). In basal patients, no difference in OS was observed between FFX and GnP groups (p=0.6). Classical patients receiving FFX had a 14.4 months median OS vs. 10.8 months for patients receiving GnP (p=0.046). Conclusions: In this real-world cohort, we validate the association between PurIST subtypes and PDAC patient survival when administered FFX or GnP. Among FFX-treated patients, classical patients had significantly better outcomes compared to basal patients. Moreover, classical patients appeared to have improved outcomes with FFX vs. GnP. These findings represent underlying biological PDAC differences and demonstrate the clinical validity of PurIST as a prognostic marker in PDAC patients when performed as an LDT on the Tempus xT platform. Ongoing evaluation of PurIST performance will be continuously monitored through the Tempus Oncology Data ecosystem. Citation Format: Stephane Wenric, James M. Davison, Yun E. Wang, Gregory M. Mayhew, Kirk Beebe, Hyunseok P. Kang, Michael V. Milburn, Vincent Chung, Tanios Bekaii-Saab, Charles M. Perou. Purity Independent Subtyping of Tumor (PurIST): Real-world data validation of a pancreatic ductal adenocarcinoma (PDAC) gene expression classifier and its prognostic implications [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A002.

S1631 Education on the Treatment of Helicobacter pylori Infection Improves Clinician Knowledge

Introduction: The prevalence of Helicobacter pylori infection in US adults is around 35%. Eradication of H. pylori is essential to the successful management of peptic ulcer disease, and the potential prevention of gastric cancer. The goal of this project was to assess the role of continuing medical education (CME) on improving learners’ knowledge regarding evidence-based treatment of H. pylori infection. Methods: Gastroenterologists (N =1499) and primary care physicians (PCPs; N =1224) participated in an online text-based CME/CE activity on the diagnosis and treatment of H. pylori infection (1). Educational effect was assessed using a repeated-pair design with pre-/post-assessment and 2 multiple choice questions. This assessed knowledge and included evaluation questions that gauged patient numbers and overall satisfaction with the education. We calculated relative improvement ([post%-pre%]/pre%) to examine change in percentage of correct responses. We used the chi-squared test for significance testing on the number of correct responses pre- to post-activity with P < 0.05 considered statistically significant. The online CME activity launched on October 15, 2021; data were collected on May 31, 2022. Results: Sixty-six percent of gastroenterologists and 60% of PCPs strongly agreed that this education increased their knowledge regarding evidence-based treatments for H. pylori infection. First line therapy for H. pylori infection: Gastroenterologists had a 308% relative increase (13%/53% pre/post; P < .05), and PCPs had a 360% relative increase (10%/46%, pre/post; P < .05) in knowledge regarding recommended first-line treatment of H. pylori infection (Figure). Antibiotic resistance and H. pylori treatment: Gastroenterologists had a 33% relative increase (55%/73%, pre/post; P < .05), and PCPs had a 162% relative increase (26%/68%, pre/post; P < .05) in knowledge about antibiotic resistance to clarithromycin in patients with H. pylori infection (Figure). Conclusion: We found significant improvement in knowledge among gastroenterologists and PCPs regarding evidence-based management of H. pylori infection. However, additional educational effort is required to close some clinical knowledge gaps regarding selection of first-line treatment for H. pylori infection and resistance to antibiotics, with particular emphasis on clarithromycin.Figure 1.: Pre and Post results for gastroenterologists and primary care physicians knowledge for first-line treatment of Helicobacter pylori.

Real-world Treatment Sequencing in Patients with Metastatic Castration-resistant Prostate Cancer: Results from the Prospective, International, Observational Prostate Cancer Registry

BackgroundProstate cancer has a multifaceted treatment pattern. Evidence is lacking for optimal treatment sequences for metastatic castration-resistant prostate cancer (mCRPC). ObjectiveTo increase the understanding of real-world treatment pathways and outcomes in patients with mCRPC. Design, setting, and participantsA prospective, noninterventional, real-world analysis of 3003 patients with mCRPC in the Prostate Cancer Registry (PCR; NCT02236637) from June 14, 2013 to July 9, 2018 was conducted. InterventionPatients received first- and second-line hormonal treatment and chemotherapy as follows: abiraterone acetate plus prednisone (abiraterone)-docetaxel (ABI-DOCE), abiraterone-enzalutamide (ABI-ENZA), abiraterone–radium-223 (ABI-RAD), docetaxel-abiraterone (DOCE-ABI), docetaxel-cabazitaxel (DOCE-CABA), docetaxel-enzalutamide (DOCE-ENZA), and enzalutamide-docetaxel (ENZA-DOCE). Outcome measurements and statistical analysisBaseline patient characteristics, quality of life, mCRPC treatments, and efficacy outcomes (progression and survival) were presented descriptively. Results and limitationsData from 727 patients were eligible for the analysis (ABI-DOCE n = 178, ABI-ENZA n = 99, ABI-RAD n = 27, DOCE-ABI n = 191, DOCE-CABA n = 74, DOCE-ENZA n = 116, and ENZA-DOCE n = 42). Demographics and disease characteristics among patients between different sequences varied greatly. Most patients who started on abiraterone or enzalutamide stopped therapy because of disease progression. No randomisation to allow treatment/sequence comparisons limited this observational study. ConclusionsThe real-world PCR data complement clinical trial data, reflecting more highly selected patient populations than seen in routine clinical practice. Baseline characteristics play a role in mCRPC first-line treatment selection, but other factors, such as treatment availability, have an impact. Efficacy observations are limited and should be interpreted with caution. Patient summaryBaseline characteristics appear to have a role in the first-line treatment selection of metastatic castration-resistant prostate cancer in the real-world setting. First-line abiraterone acetate plus prednisone seems to be the preferred treatment option for older patients and those with lower Gleason scores, first-line docetaxel for younger patients and those with more advanced disease, and first-line enzalutamide for patients with fewer metastases and more favourable performance status. The benefit to patients from these observations remains unknown.

EP11.01-004 An Effective Two-step Reflex Test for 10 Biomarkers Analysis in Non-small Cell Lung Cancer

The treatment of non-small cell lung cancer (NSCLC) has become increasingly biomarker-driven over the last decade, with a growing number of targeted therapies available for patients with specific genomic alterations. A fast-track screening of multiple molecular biomarkers is therefore required for first-line treatment selection in NSCLC, although challenging considering both turnaround time and sample quality. In an effort to improve the molecular diagnostic workup at our institution, we implemented a two-step reflex test (RT) at the time of pathological diagnosis of non-squamous NSCLC for EGFR, KRAS, BRAF, ERBB2, ALK, ROS1, PDL-1, RET, MET, and NTRK testing.

Diagnostic Value and Cost-Effectiveness of Next-Generation Sequencing–Based Testing for Treatment of Patients with Advanced/Metastatic Non-Squamous Non–Small-Cell Lung Cancer in the United States

The study evaluated the diagnostic value and cost-effectiveness of next-generation sequencing (NGS)-based testing versus various combinations of single-gene tests (SGTs) for selection of first-line treatment for patients with advanced/metastatic non-squamous non-small-cell lung cancer in the United States. A dynamic decision analysis model was developed comparing NGS versus SGT from a payer perspective. Inputs were obtained from published sources and included diagnostic performance, biomarker-positive disease rates, biomarker-directed recommendations for treatment, and survival outcomes. Costs were reported in 2020 US dollars. In the base case, NGS improved the detection of actionable biomarkers by 74.4%, increased the proportion of patients receiving biomarker-driven therapy by 11.9%, and decreased the proportion of patients with biomarker-positive disease receiving non-biomarker-driven first-line treatment by 40.5%. The incremental cost-effectiveness ratio per life-year gained of NGS testing versus SGT was $7224 (excluding post-diagnostic costs); the incremental cost-effectiveness ratio for NGS-directed therapy was $148,786 versus SGT-directed therapy. Sensitivity analyses confirmed the robustness of these findings; survival outcomes and targeted therapy costs had the greatest impact on results. Testing strategies with NGS are more comprehensive in the detection of actionable biomarkers and can improve the proportion of patients receiving biomarker-driven therapies. NGS testing may provide a cost-effective strategy for advanced/metastatic non-squamous non-small-cell lung cancer; however, the value of NGS-directed therapy varies by the willingness-to-pay threshold of the decision-maker.

A Prospective Multicenter Evaluation of Initial Treatment Choice in Metastatic Renal Cell Carcinoma Prior to the Immunotherapy Era: The MaRCC Registry Experience.

The Metastatic Renal Cell Carcinoma (MaRCC) Registry provides prospective data on real-world treatment patterns and outcomes in patients with metastatic renal cell carcinoma (mRCC). Patients with mRCC and no prior systemic therapy were enrolled at academic and community sites. End of study data collection was in March 2019. Outcomes included overall survival (OS). A survey of treating physicians assessed reasons for treatment initiations and discontinuations. Overall, 376 patients with mRCC initiated first-line therapy; 171 (45.5%) received pazopanib, 75 (19.9%) sunitinib, and 74 (19.7%) participated in a clinical trial. Median (95% confidence interval) OS was longest in the clinical trial group (50.3 [35.8-not reached] months) versus pazopanib (39.0 [29.7-50.9] months) and sunitinib 26.2 [19.9-61.5] months). Non-clear cell RCC (21.5% of patients) was associated with worse median OS than clear cell RCC (18.0 vs. 47.3 months). Differences in baseline characteristics, treatment starting dose, and relative dose exposure among treatment groups suggest selection bias. Survey results revealed a de-emphasis on quality of life, toxicity, and patient preference compared with efficacy in treatment selection. The MaRCC Registry gives insights into real-world first-line treatment selection, outcomes, and physician rationale regarding initial treatment selection prior to the immunotherapy era. Differences in outcomes between clinical trial and off-study patients reflect the difficulty in translating trial results to real-world patients, and emphasize the need to broaden clinical trial eligibility. Physician emphasis on efficacy over quality of life and toxicity suggests more data and education are needed regarding these endpoints.