Tsai, Jill

Background: Longitudinal genomic profiling through serial circulating tumor DNA (ctDNA) testing offers a noninvasive method to monitor clonal evolution in advanced prostate cancer. This study evaluated the frequency and nature of newly emergent and potentially actionable genomic alterations detected through serial testing in a real-world setting. Methods: We conducted a retrospective analysis of advanced prostate cancer patients who underwent multiple Guardant360 ctDNA tests between October 2020 and March 2023. The study focused on identifying new genomic alterations absent in the initial test, with particular attention to alterations relevant for on-label therapies, therapies approved in other oncologic indications (i.e., off-label), or a clinical trial. Results: Among 479 patients with at least two ctDNA tests, the median interval between the first and second evaluable tests was 207 days. New and potentially actionable alterations emerged in 57.8% of patients, including potential targets for on-label therapies (16.7%), off-label therapies (16.5%), and clinical trials (55.7%). Tumor mutational burden (TMB) increased from “low” to “high” in 11% of patients, although none had microsatellite instability or mismatch repair deficiency. In a Mayo Clinic subset, ten patients received olaparib based on treatment-emergent alterations, but none achieved a prostate-specific antigen (PSA) response. Two patients who transitioned from low to high TMB received pembrolizumab, both with progressive disease as best response. Conclusions: In a large real-world cohort, serial ctDNA testing frequently identified new alterations that were not detected at baseline and are potentially actionable therapeutic targets, highlighting the value of serial genomic profiling for capturing clonal dynamics. Additional research is needed to better establish a framework for retesting and to clarify how these results should influence subsequent treatment decisions.

Abstract Purpose: KRAS G12C inhibitors can treat KRASG12C-mutant advanced colorectal cancers and pancreatic ductal adenocarcinomas (PDAC), but alterations in Kirsten rat sarcoma (KRAS), EGFR, BRAF, MAP2K1, and other genes bypass KRAS inhibition and reduce therapy efficacy. Our study evaluates the genetic landscape of candidate primary resistance alterations relevant to KRAS targeting in KRASG12C-mutant colorectal cancer and PDAC. Experimental Design: We analyzed two cohorts (national database and Mayo) of patients with advanced colorectal cancer or PDAC tested with next-generation sequencing of ctDNA via Guardant360. Cohorts were divided into three groups: KRASG12C alone (KRASG12C without a resistance gene), KRASG12C with resistance (KRASG12C and ≥1 candidate resistance gene), and KRAS not detected. Candidate resistance mutations were inferred from the reported literature. Results: Among the national (13,603 colorectal cancer and 5,016 PDAC cases) and Mayo (741 colorectal cancer and 422 PDAC cases) cohorts, resistance alterations were identified in a considerable number of KRASG12C cases (46.5% of national colorectal cancer, 16.4% of national PDAC, 53.8% of Mayo colorectal cancer, and 36.4% of Mayo PDAC). The presence of resistance alterations was associated with a trend toward worse overall survival in KRASG12C colorectal cancer (P = 0.05). Conclusions: Putative resistance alterations are prevalent in PDAC and colorectal cancer and may limit monotherapy efficacy. Identifying these alterations has potential implications in optimal patient selection for targeted therapies and the development of combination therapy strategies to overcome primary resistance.

3519 Background: KRAS G12D commonly occurs in pancreatic (PDAC) and colorectal (CRC) cancers. Though developing KRAS G12D inhibitors is pertinent, it is expected that primary and acquired resistance will limit efficacy, similar to those seen with KRAS G12C inhibitors. Here, we assessed the prevalence and outcomes of candidate mutations co-occurring with KRAS G12D in PDAC and CRC using liquid biopsy data. Methods: Patients with advanced CRC and PDAC tested with Guardant360 genomic profiling from 2020 to 2023 (~2500 & 3500 samples) were included. Candidate resistance mutations were determined based on mutations reported following KRAS G12C inhibition. Patients with advanced CRC and PDAC tested with Guardant360 at Mayo Clinic (2016-2023) (1158 & 904 samples) were assessed for overall survival (OS). Patients were divided into three groups: KRAS G12D alone, KRAS G12D with candidate resistance alterations, and KRAS G12D not detected (ND). OS was estimated using Kaplan-Meier method. Cox regression was used for multivariable survival analyses. Results: The Guardant360 database reported KRAS G12D in 16.5% of CRC and 40.2% of PDAC samples. The Table below summarizes the most frequent candidate resistance alterations co-occurring with KRAS G12Dincluding point mutations of KRASand PIK3CAand amplifications of EGFR, KRAS, BRAFand MYC. In the Mayo cohort, 8.9% of CRC and 23.5% of PDAC patients had KRAS G12D. In CRC, those with KRAS G12Dand ≥1 candidate resistance alteration had a significantly lower OS (mOS 18.7m) than KRAS G12D alone (30.9m), and ND (37.3m) ( p=0.004). The presence of concurrent candidate resistance alterations with KRAS G12D remained a poor prognostic factor with HR 2.1 (95% CI: 1.3 – 3.2, p=0.001) after adjusting for age and other patient characteristics. Similarly, in PDAC, those with KRAS G12D and ≥1 resistance mutation had significantly lower OS (mOS 8.1m) than KRAS G12D alone (10.5m) and ND (16.1m) (p<0.0001). In multivariable analyses, both KRAS G12D with candidate resistance alterations (HR 2.1, 95% CI: 1.4-3.3, p<0.001) and G12Dalone (HR 1.6; 1.3–2.0, p<0.001) were poor prognostic factors. Among patients with serial Guardant360, treatment response trended with KRAS G12D variant allele frequency. Conclusions: Circulating tumor DNA testing is a useful tool to reveal candidate molecular alterations linked to potential resistance to KRAS G12D targeting in patients with KRAS G12D gastrointestinal cancers. KRAS G12D with these concurrent molecular alterations is a poor prognostic factor in both PDAC and CRC. [Table: see text]

1037 Background: TBCRC041, a randomized phase 2 trial of alisertib (A) ± fulvestrant (F) in women with HR+/HER2- MBC demonstrated promising clinical activity for alisertib (1). Interrogation of baseline (PreC1) and end of cycle 1 (EOC1) cfDNA and CTCs may identify biomarkers associated with response to A±F. Methods: Plasma cfDNA was sequenced using the Guardant INFINITY platform, which includes genomic and methylated tumor fraction (mTF) analysis. Pathogenic variants were determined using publicly available databases and Mayo Clinic annotation pipelines. CTCs were identified as nucleated, EpCAM+/ cytokeratin+/ CD45- cells and assessed for ER/HER2 staining (RareCyte, Seattle). PFS was defined as the time from randomization to progression or death. EOC1-PFS differs from PFS as time starts at C2D1. Stratified log-rank tests and conditional Cox modeling were used to assess whether PFS or EOC1-PFS differed by mutations in ESR1 or PIK3CA, pre-CTC ≥ 5, or mTF>1%. Results: Of the 91 eligible pts, 84 progressed, and 73 were deceased. In the 86 pts with PreC1-cfDNA results, variants were identified in ESR1 (42; 48.8%), PIK3CA (36; 41.9%), AKT1 (7; 8.1%), PTEN(7; 8.1%), BRCA1 (4; 4.7%), and BRCA2 (7; 8.1%). Pts with a PIK3CAmut had significantly decreased PFS (HR 1.8; 95%CI: 1.1 -2.9, p=0.014) but not by ESR1mut status (p=0.583). PreC1 and EOC1-mTF were determined for 78 and 76 pts, respectively. PreC1-mTF was 0-1% in 21; 1.1-10.0% in 27; and 10.1-100% in 30. mTF levels remained between 0-1% in 17 pts fell to 0-1% in 11 pts, and was 0-1% in 5 pts without a preC1-mTF determination. PFS did not differ with respect to PreC1-mTF (p=0.210), but EOC1-PFS was worse in pts with mTF >1% (HR 3.0; 95%CI: 1.6 -5.2, p<0.001). PreC1 and EOC1-CTC were determined for 78 and 76 pts. 36 (46.2%) pts had a PreC1 CTC count ≥ 5. EOC1-CTC levels remained at (n=17) or increased to (n=1) ≥ 5 and was ≥ 5 in 2 pts without a preC1-CTC determination. EOC1-PFS was not significantly different for EOC1-CTC ≥ 5 (p=0.065), but was significantly decreased in pts with PreC1-CTC≥5 (HR=1.8; 95%CI: 1.1-3.0; p=0.018). Broader cfDNA NGS panel and CTC-derived ER/HER2 staining results will be presented at the meeting. Conclusions: Among patients receiving A±F, PFS varied based on the PIK3CAmutation status. cfDNA and CTC assessment PreC1 and EOC1 provided complementary information. Further validation in larger datasets is needed. 1. JAMA Oncol 2023;9:815. Clinical trial information: NCT02860000 .

Abstract Background: Non-small cell lung cancer (NSCLC) makes up ~85% of lung cancers, and its major subtypes are adenocarcinoma (AD) and squamous cell carcinoma (SC). The histology classification of NSCLC has significant therapeutic and prognostic implications. As liquid biopsies are rapid and non-invasive, computational methods to classify NSCLC subtypes using cell-free DNA (cfDNA) are desired. Methods: Using The Cancer Genome Atlas (TCGA) NSCLC methylation data, we developed a method to classify AD and SC subtypes from cfDNA samples. We mapped TCGA methylation data to Guardant Infinity’s promoter regions and transformed them to allow for the transfer of the TCGA trained model to Infinity cfDNA epigenomic data. An advantage of this approach is that TCGA training data originates from the primary tumor, while cfDNA samples have low tumor fraction (TF) and may also contain signals from metastatic tumors. We trained a penalized Logistic Regression model with LASSO. To better fit the model to Infinity cfDNA data, we performed region filtering. Results: Our model effectively classified AD and SC subtypes from TCGA samples (AUC 0.97) (10-fold cross validation; Table 1). When evaluating on cfDNA samples with epigenomic TF ≥ 1%, the final model shows reasonable performance (AUC 0.94) (Table 1); the performance decreases when reduced to TF &gt; 0.05% (AUC 0.89) as expected (Table 1). When evaluating cfDNA from uncategorized lung samples, the predicted AD probabilities have significant association with KRAS mutations (p-value = 0.027). Conclusion: Our results show that the penalized Logistic Regression model trained with tissue TCGA methylation data through mapping, transformation, and region selection can classify NSCLC subtypes from cfDNA samples. This method may offer a fast, non-invasive tool to inform subtype-based clinical decisions, especially in the setting of poorly-differentiated carcinomas, and to identify subtype switches upon resistance to targeted therapies. Table 1: Model evaluation on tissue TCGA samples and cfDNA Infinity samples (different TF cutoffs) Sample type N samples AUC Accuracy Sensitivity Specificity Precision F1 score tissue TCGA 907 0.97 0.93 0.94 +/- 0.02 0.93 +/- 0.02 0.94 +/- 0.02 0.94 +/- 0.01 cfDNA Infinity 17 (TF ≥ 1%) 0.94 0.88 0.83 +/- 0.3 0.91 +/- 0.17 0.83 +/- 0.3 0.83 +/- 0.21 cfDNA Infinity 29 (TF &gt; 0.05%) 0.89 0.83 0.88 +/- 0.23 0.81 +/- 0.17 0.64 +/- 0.28 0.74 +/- 0.2 Citation Format: Laura Tung, Denis Tolkunov, Catalin Barbacioru, Deepika Kale, Scott Shell, Jill Tsai, Leylah Drusbosky, Kaushal Parikh, Han-Yu Chuang. Non-small cell lung cancer (NSCLC) histology classification using DNA methylation data captured from liquid biopsies [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 3499.

170 Background: KRAS-mutant colorectal cancer (CRC) has a worse prognosis and greater resistance to therapy, attracting tremendous efforts to develop both allele-specific and pan-KRAS targeting agents including KRAS G12C and G12D inhibitors. CRC has demonstrated both primary and acquired resistance to KRAS G12C inhibitors, which can be better evaluated by circulating tumor DNA (ctDNA)-based next generation sequencing (NGS) methods without tissue sampling bias. Here, we utilized clinical ctDNA data to delineate the landscape of candidate alterations associated with primary resistance to KRAS G12D targeting in CRC. Methods: Samples from advanced CRC patients with Guardant360 data in 2021 were categorized into a KRAS G12D group and comparator groups of KRAS G12C and KRAS not detected (ND) according to KRAS mutation status. Demographics, MSI-H status, and blood tumor mutational burden (bTMB) were summarized and compared between groups using t-test for continuous variables and Fisher’s exact test for categorical variables. Co-occurrence patterns of alterations between KRAS and other genes were assessed. Primary resistance mutations were inferred by acquired resistance findings to KRAS G12C inhibitors reported by Awad et al. N Engl J Med. 2021 and Tanaka et al. Cancer Discov. 2021. Results: Among approximately 2500 samples, 16.8% had KRAS G12D, the most common KRAS mutation in CRC. The mean allele frequency of KRAS G12D (11.4%) is comparable to that of KRAS G12C (10.9%). Mean age (61.6 years), MSI-H frequency (3.8%), and mean bTMB (14.2 Mut/Mb) of KRAS G12D group is comparable to those of KRAS ND group. This contrasts with KRAS G12C with significantly lower mean age (60.2 years, p=0.01), lower MSI-H frequency (0.56%, p=0.03), and higher mean bTMB (16.3 Mut/Mb, p=0.02) compared to KRAS ND. Both KRAS G12D and G12C significantly co-occur with APC, PIK3CA, and SMAD4 alterations. FBXW7, AR, and KEAP1 alterations were unique co-occurrences to KRAS G12D, while KIT was unique to KRAS G12C. Candidate alterations associated with primary resistance in KRAS G12D CRC included simultaneously present mutations of KRAS Q61H (14.1%), G12V (6.4%), G12C (5.9%), G13D (3.6%), BRAF V600E (2.7%) and others, amplification of MET (2%), MYC (1.6%), and KRAS (1.4%), and FGFR3-TACC3 translocation (Table). Conclusions: KRAS G12D is associated with unique co-occurring molecular alterations compared to KRAS G12C in CRC. ctDNA-based NGS platforms can survey candidate alterations associated with primary resistance to KRAS G12D targeting in CRC. Further validation in preclinical models is needed. [Table: see text]

239 Background: In part, TMB guides therapeutic decisions on the use of immune checkpoint inhibitors (ICIs) in aPC. Most patients (pts) with aPC do not have high TMB (TMB-H) at the time of initial assessment. However, TMB changes over time (from treatment pressure) as pts progress through lines of therapy (LOT) has not been extensively explored in aPC. We describe real world data on changes to blood TMB (bTMB) in pts with aPC exposed to several LOT. Methods: Genomic results from pts with aPC who had ≥1 ctDNA test(s) with TMB analyzed (Guardant360, Guardant Health, Inc) as part of clinical care from Oct 2020 – Jun 2022 were retrospectively queried. Clinical factors were extracted from test requisition forms; pts from Mayo Clinic had additional factors, including intervening treatment, extracted from medical records. High bTMB (TMB-H) was defined as ≥13.4 mut/Mb, as previously reported. Proportions were compared using Fisher’s Exact Test. Results: 316 patients with a median age of 72 (range: 44-91) were analyzed. Initial median TMB for the entire cohort was 8.18 mut/Mb (range: 0.01-268.3). On subsequent test, TMB decreased in 122(40%), increased in 173(55%), of whom 29 (17%) became TMB-H; 21 (7%) had unchanged TMB. Median time between tests was 182 days (range: 21-578). 101 patients with 262 tests and a median age of 71 years (range: 46-87) were clinically annotated in the Mayo cohort of whom 58 had >2 consecutive TMB scores: 25 (43%) had TMB increase, of whom 4 (16%) became TMB-H, 23 (40%) had TMB decrease, 10 (17%) had dynamic changes with increases and decreases. Numeric but not statistically significant differences in clinical factors were observed between groups (p>0.05). Conclusions: In this real world cohort, variation in TMB is seen with increasing LOT. Importantly, nearly 1 in 5 patients with increasing tumor mutational burden develop TMB-H, which may be a clinically actionable finding as these pts become eligible for ICI. Findings underscore the importance of repeat genetic testing with time and LOT. [Table: see text]

600 Background: Hepatocellular carcinoma (HCC) is the most common primary liver cancer and is associated with a high mortality burden. Lenvatinib is an effective frontline tyrosine kinase inhibitor for HCC but is associated with primary and acquired resistance in most patients. Molecular mechanisms of resistance to lenvatinib remain unclear. Methods: We retrospectively analyzed 227 patients with HCC who underwent baseline ctDNA profiling as a component of routine clinical care, including 46 patients who had serial ctDNA analysis done at baseline and after progression to lenvatinib or ICI. Clinical data including demographics, treatment history, tumor responses, and survival outcomes were abstracted from the electronic medical record. Tumor response was categorized according to RECIST 1.1. A national registry of HCC that underwent ctDNA testing using Guardant360 was analyzed to determine the prevalence of EGFR/ERBB2 alterations in HCC. Results: Nearly a third of patients who progressed on lenvatinib demonstrated a gain in EGFR/ ERBB2 amplifications or activating mutations. No consistent trend in EGFR/ ERBB2 amplifications and alterations was found among ICI refractory HCC. Disease control rates (including complete response, partial response, and stable disease) were 20% in EGFR/ERBB2 altered HCC treated with lenvatinib, 62.5% in EGFR/ERBB2 wildtype HCC treated with lenvatinib (p = 0.05), and 58% in EGFR/ERBB2 altered HCC treated with ICIs (p = 0.09). PFS was also longer in EGFR/ERBB2 wildtype HCC treated with lenvatinib (5.9 months) and EGFR/ERBB2 altered HCC treated with ICIs (7.1 months) compared to EGFR/ERBB2 altered HCC treated with lenvatinib (2.2 months, p = 0.002). In a national cohort of 1739 patients with HCC, EGFR amplifications and activating mutations occurred in 6.5% and 1.5% of cases, respectively, and ERBB2 amplifications and activating mutations occurred in 1.9% and 0.7% of cases, respectively. Among EGFR/ERBB2 mutations, we identified recurrent gatekeeper mutations, including multiple A547T and T790M mutations, in 31% of patients. Conclusions: Our study substantiates EGFR/ERBB2 amplifications and mutations as putative drivers of lenvatinib resistance in patients with HCC and identifies the genetic mechanisms for EGFR pathway activation in refractory cancers. EGFR/ERBB2 alterations may thus represent predictive and pharmacodynamic markers of lenvatinib resistance. These findings lend support to the use of EGFR inhibitors combined with lenvatinib in previously untreated or lenvatinib refractory cancers.