Citations

3525 Background: Tumor microenvironment (TME) features of colorectal cancer (CRC) may influence detection of circulating tumor DNA (ctDNA) and further refine prognosis. We applied a deep learning histopathologic algorithm to quantify TME features in stage III colon cancers and evaluated their association with postoperative ctDNA status among participants in a phase III FOLFOX-based adjuvant trial (NCCTG N0147). Methods: QuantCRC (Aiforia Technologies) was applied to digitized hematoxylin and eosin-stained whole-slide images of stage III colon adenocarcinomas to extract quantitative histopathologic features. Among cases where data met quality control (N=1817), associations between 15 distinct pathologist-defined features and clinical outcomes—time to recurrence (TTR), disease-free survival (DFS), and overall survival (OS)—were evaluated, stratified by postoperative ctDNA status assessed by a tissue-free assay (Guardant Reveal). Relationships were analyzed using univariate and multivariable Cox proportional hazards models that were adjusted for clinicopathologic and molecular features ( KRAS , BRAF V600E , MMR status). Interaction testing was performed. Results: We found statistically significant quantitative differences for histopathological features by ctDNA status. Features associated with ctDNA positivity(+) included lymphovascular invasion (LVI), and increased %high grade, %tumor budding, %necrosis, and %stroma. ctDNA+ cases had lower %inflammatory stroma and fewer %signet ring cells (all p< 0.006). Multivariable analyses, irrespective of ctDNA status, identified %tumor budding, %mucin, %signet ring cells, %inflammatory tumor bed, and %inflammatory stroma as significantly associated with adverse outcomes (TTR, DFS, OS). Within ctDNA+ cases, higher %immature tumor bed was associated with adverse outcome for all 3 variables. Among ctDNA-negative cases, significantly poorer outcomes (TTR, DFS, OS) were observed for tumors with lower tumor infiltrating lymphocyte (TIL) density and reduced %necrosis. After adjustment, significant interactions with ctDNA status were observed for LVI (p< 0.006) and %signet ring cells (p< 0.035) across all outcome variables, for %necrosis with DFS, OS (p<0.040), and for TILs with TTR (p<0.015). Analysis is ongoing for ctDNA tumor fraction as well as tumor genotyping data (739 genes; Guardant360). Conclusions: QuantCRC identifies TME features associated with postoperative ctDNA status that enable risk-stratification within ctDNA groups. Among ctDNA-negative patients, reduced intratumoral TIL density and reduced tumor necrosis identify high-risk subgroups with inferior clinical outcomes, and may have clinical utility.

PURPOSE Detection of molecular residual disease using circulating tumor DNA (ctDNA) may enable postoperative risk stratification and guide adjuvant therapy. We evaluated the prognostic value of a tissue-free, epigenomic ctDNA assay in patients with stage III colon cancer (CC) enrolled in a phase III adjuvant chemotherapy trial. METHODS Plasma samples were collected after surgery and before adjuvant infusional fluorouracil, leucovorin, and oxaliplatin alone or combined with cetuximab. ctDNA was analyzed using a tissue-free assay; in ctDNA-positive patients, tumor fraction (TF) was quantified and genotyping was performed with a 739-gene panel. Associations with disease-free survival (DFS), time to recurrence (TTR), and overall survival (OS) were assessed using multivariable Cox models adjusted for covariates. RESULTS Among 2,260 evaluable patients, 461 (20.4%) were ctDNA-positive with significantly higher detection in advanced T-/N-stage, high-grade, obstruction/perforation, and BRAF V600E tumors. At a median follow-up of 6.1 years, ctDNA positivity was independently associated with shorter TTR (hazard ratio [HR], 5.96 [95% CI, 5.11 to 6.96]), DFS (HR, 5.03 [95% CI, 4.36 to 5.81]), and OS (HR, 4.45 [95% CI, 3.76 to 5.27]; all P < .0001). The 5-year DFS was 27.7% (95% CI, 23.8 to 32.2) v 77.1% (95% CI, 75.1 to 79.1) in ctDNA-positive versus ctDNA-negative patients, and adverse prognostic impact was greater in lower T/N stage, low-risk, and dMMR subsets (interaction P = .0012-.041). Among ctDNA-positive patients, TF was nearly double in those who recurred or died ( P = .0002) and stratified patients for TTR, DFS, and OS (all adjusted P < .002). Genotyping identified mutations in FLT1 (OR, 8.99) and PREX2 (OR, 7.73) genes that were most strongly associated with recurrence ( P < .03). CONCLUSION Evaluation of ctDNA in resected stage III CC using a tissue-free assay provided robust and independent prognostic value. Higher ctDNA burden, dMMR, and specific mutations defined poor prognostic groups among ctDNA-positive patients.

Importance The definition of cure in stage II to III colon cancer (CC) remains unclear due to limitations in conventional end points, which include deaths and second primary tumors as events. These can complicate communication with patients regarding long-term outcomes. Objective To distinguish relapses from competing health-related events to classify long-term outcomes years after surgery and explore when the incidence of true relapses of the initial CC approaches 0% to define cure in this disease. Design, Setting, and Participants This pooled analysis of individual patient–level data from 15 phase 3 randomized clinical trials assessed time to CC-related relapse using Kaplan-Meier and Aalen-Johansen methods, with death and second primary tumors treated as competing risks. Cox regression models evaluated prognostic associations, stratified by sex, stage, and tumor. Patients with stage II to III CC who underwent adjuvant chemotherapy were included. All patients had undergone radical surgery for CC and received adjuvant chemotherapy with a median follow-up of at least 6 years. The Adjuvant Colon Cancer Endpoints (ACCENT) and the International Duration Evaluation of Adjuvant Chemotherapy (IDEA) databases included adjuvant studies conducted between 1996 and 2015. Data were analyzed from February 2022 to June 2025. Exposures Adjuvant chemotherapy regimens varied across trials, including fluoropyrimidines alone or in combination with oxaliplatin or biologic agents. Main Outcomes and Measures The primary outcome was time to CC-related recurrence. The predefined threshold for cure was a recurrence risk below 0.5%. Results Of 35 213 included patients, 19 346 (54.9%) were male, and the mean (SD) age was 60.2 (10.8) years. The incidence rate of recurrence peaked at 6.4% (1993 of 31 373) between month 6 and month 12 and decreased continuously until year 10 of follow-up never exceeding 0.5%. Recurrence rate appeared to increase again after year 10 and peaked at 2.0% during year 12.5 to year 13, a pattern observed exclusively in the MOSAIC trial. Competing-event analysis revealed that death and second primary tumors inflated the apparent recurrence rate, especially for older patients. The overall cumulative incidence of relapse with death as competing risk was lower among female patients (hazard ratio, 0.58; 95% CI, 0.45-0.76; P &amp;lt; .001). Conclusions and Relevance In this pooled analysis of phase 3 randomized clinical trials, a recurrence rate less than 0.5% occurred after 6 years from surgery, supporting a practical definition of cure. Recognizing this milestone may improve patient communication, guide follow-up duration, and reduce unnecessary long-term surveillance.

3520 Background: Recent analyses highlight nonhierarchical outcomes using the 8 th Edition AJCC staging system for CC. For instance, the 5-year survival rate for stages I and IIIa patients (pts) closely align. Additionally, tumor deposits (TDs) have been established as significant prognostic indicators. The AJCCCCEP commissioned this study to develop an updated pathological staging system for CC focused specifically on pts without distant metastasis (M0), while retaining the existing stage IV classification. Methods: Individual patient data (IPD) from pts diagnosed with cc (2010- 2017) in the NCDB were divided into training (70%) and internal validation (30%) datasets. External validation used IPD from clinical trials. The primary endpoint was overall survival (OS). Risk classification development for M0 pts incorporated ungrouped data on pathologic T categories, the number of involved regional lymph nodes (LN+), and TD counts. Recursive partitioning and regression tree analyses were applied to construct hierarchical staging levels. Pre-specified criteria required survival probabilities to be consecutive and show clear separations using Kaplan-Meier (KM) estimates with pairwise log-rank test P of < 0.005 for the training and < 0.05 for validation analyses. Results: Data from 281,997 pts (median age 67 years, 50% male, 81% white, 55% T3, 19% T4, 44% N+, 26% M+, and 11% with ≥1 TD) were analyzed, with a median follow-up of 7.3 years. The updated staging system (Table) met pre-specified criteria, with all observed pairwise P < 0.0001 in the development and internal validation sets. KM OS curves displayed a hierarchical separation across all sub-levels after the 1 st year of diagnosis. Consistent results were seen in pts treated with adjuvant chemotherapy in 4 trials (all pair-wise P < 0.0001). Conclusions: The proposed pathological staging system for M0 pts fulfills pre-specified criteria for hierarchical risk stratification, validated both internally and externally, and provides an evidence-based update. Pending review process, the AJCCCCEP will recommend that these changes be made to the Version 9 staging protocol for colon cancer to improve prognostication for CC pts. Stage T, # of LN+, # of TD M % of pts 1y OS (CI), % 3y OS (CI), % 5y OS (CI), % I T1, 0, 0 0 5 96 (95-97) 91 (90-92) 84 (83-86) IIa T2, 0, 0 0 10 95 (94-95) 88 (88-89) 80 (79-81) IIb T1, 0, 1+T1, 1+, 0T2, 0, 1+T2, 1-4, 0T3, 0, 0 0 27 93 (92-93) 84 (84-85) 75 (75-76) IIIa T1, 1+, 1+T2, 1-4, 1+T2, 5+, 0T3, 0, 1+T3, 1-4, 0 0 14 92 (91-92) 80 (80-81) 71 (70-72) IIIb T2, 5+, 1+T3, 1-4, 1+T3, 5+, 0T4a, 0-4, 0T4b, 0-2, 0 0 13 86 (86-87) 69 (68-70) 58 (57-59) IIIc T3, 5+, 1+T4a, 0-4, 1+T4a, 5+, anyT4b, 0-2, 1+T4b, 3+, any 0 5 78 (77-80) 53 (51-54) 40 (38-41) IVa Any 1a 19 59 (58-60) 28 (28-29) 17 (16-18) IVb Any 1b 7 43 (42-44) 14 (13-14) 6 (6-7) CI: 95% confidence internal; Peritoneum involvement data were not available before 2018 in NCDB. Thus, IVa/b were based on 7 th Edition.

3504 Background: Among patients with resected node-positive colon cancer, nearly 30% will relapse despite standard adjuvant chemotherapy. Analysis of molecular residual disease (MRD) using circulating tumor DNA (ctDNA) may enable risk stratification for tumor recurrence and inform adjuvant treatment decisions. Methods: Postsurgical ctDNA was analyzed in patients with stage III colon carcinoma who participated in a phase 3 trial of adjuvant FOLFOX alone or combined with cetuximab (n = 3084) [NCCTG N0147]. We utilized a tissue-free epigenomic assay for ctDNA detection (Guardant Reveal) with sampling prior to start of adjuvant therapy. Among ctDNA positives, epigenomic tumor fraction (TF) was estimated and ctDNA genotyping was done with Guardant360 (panel of 739 genes). Median follow-up was 6.1 years (yr). Study endpoints included time-to-recurrence (TTR), disease-free survival (DFS) and overall survival (OS) analyzed by Kaplan–Meier method. Multivariable Cox proportional hazards models were used to assess prognostic utility of ctDNA status adjusting for confounders. Interaction between ctDNA and clinicopathological features were assessed. Results: Among 2260 patients with evaluable ctDNA data, 461 (20.4%) were ctDNA positive.Tumors were significantly associated with higher T, N stage, BRAF V600E , high grade, obstruction/perforation, and worse performance status. Positive vs negative ctDNA was significantly associated with shorter TTR (hazard ratio [HR] 4.33, 95% confidence interval [CI] 3.65-5.13, P < 0.0001), poorer DFS (HR 3.74, CI 3.18-4.39, P < 0.0001] and OS (HR 3.17, CI 2.63-3.83, P < 0.0001), adjusting for covariates and tissue MMR, KRAS and BRAF V600E . ctDNA positive vs negative patients had 3y DFS of 36.4% (95%CI 32.2-41.2%) vs 82.5% (95% CI 80.0-84.4%), respectively. Adverse prognosis was consistent across subgroups (all P < 0.05), with stronger detrimental effects for positive ctDNA in N1 (vs. N2), T1/2 (vs T3 or 4), and mismatch repair deficient tumors [interaction P = 0.0002 to 0.041). Among patients with positive ctDNA, TF in those who recurred/died within 3 yr was double of those who remained recurrence-free ( P = 0.0001). High vs. low ctDNA TF ( > vs ≤ median) further stratified TTR (HR 1.48, CI 1.17-1.88, P = 0.0011), DFS (HR 1.52, CI 1.21-1.92, P = 0.0004) and OS (HR 1.58, CI 1.21-2.07, P = 0.0009), adjusting for confounders. ctDNA positive cases, . Analyses of ctDNA detection by site of recurrence, ctDNA clearance, and genomic variant detection are ongoing. Conclusions: In the largest study evaluating tissue-free epigenomic-based MRD detection, we demonstrate a robust prognostic utility of postsurgical ctDNA. Tumor fraction provided further patient stratification and analysis is ongoing to identify a subgroup based on TF that may be unlikely to clear ctDNA despite adjuvant chemotherapy.

Abstract Despite advances in surgery and chemotherapy, patients with resectable pancreatic ductal adenocarcinoma (PDAC) face high recurrence rates (40-60%). Total neoadjuvant therapy (TNT) seeks to enhance margin-negative resections (R0) and address micrometastatic disease, with potential survival benefits. Yet, TNT still results in high recurrence rates, with 1-, 2-, and 3-year disease-free survival (DFS) at 65%, 48%, and 32%, respectively. Residual cancer extent is a key prognostic factor in PDAC after TNT. Tumor response scores show favorable outcomes only for patients achieving near-complete or complete pathologic response. In a cohort of 511 PDAC patients treated with TNT and surgery (2015-2022), improved DFS was only found in those with a complete pathologic response (pCR, 7.8%). This highlights the limited use of current response scoring in stratifying post-TNT patient risk, underscoring the need to better understand disease persistence and relapse. Here we developed a quantitative method to assess residual cancer. Using a digital pathology H&E library from 2180 PDAC cases (2000-2022), an AI platform segmented tissue into cancer epithelium, stroma, and normal regions. For 202 TNT-treated cases without pCR, we calculated a ratio of cancer area to stromal area from representative slides. Modeled after the histologic tumor regression grade (HTRG) system, cases were classified into &lt;5%, 5-50%, or &gt;50% cancer-to-stroma ratios. However, this measure alone showed no difference in DFS, suggesting tumor burden cannot fully explain differences in clinical outcomes. We hypothesized that the spatial organization of the tumor microenvironment (TME) is a determinant of patient outcomes. Drawing from landscape ecology, which studies spatial properties of ecosystems, we applied similar principles to analyze tissue architecture. Using an AI-based two-class segmentation of whole-slide images, we assessed spatial features like patch area, perimeter, density, and Shannon diversity. Python’s ‘pylandstats’ package calculated 21 spatial metrics, reduced via stepwise Cox regression using the Akaike information criterion (AIC). The final model included mean Shannon diversity, mean and standard deviation of stromal patch areas, and stromal patch density. Kaplan-Meier analysis of 202 samples stratified model predictions into quartiles, correlating with high (mean DFS 10.85 months), medium (19.32 months), and low risk (25.92 months) groups. These findings aligned with model expectations, underscoring the potential of spatial TME analysis to predict outcomes. Citation Format: Andrea V. Maraone, Zafar Siddiqui, Luis H. Cisneros, Merih D. Toruner, Mark Truty, Cornelius Thiels, Robert R. McWilliams, Steven R. Alberts, Rondell P. Graham, Carlo Maley, Qian Shi, Martin E. Fernandez-Zapico, Ryan M. Carr. Tumor spatial configuration predicts pancreatic cancer recurrence risk after total neoadjuvant therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5070.

727 Background: PARP inhibition (PARPi) is a standard maintenance therapy for patients (pts) with germline BRCA1/2 mutations in pancreatic cancer. Combination of PARPi therapy and immunotherapy has potential to increase efficacy and may offer benefit beyond germline BRCA1/2 . Methods: We performed a multisite single-arm phase 2 study using the highly potent PARPi niraparib with anti-PD1 drug dostarlimab in pts with germline or somatic HRD mutations in BRCA1/2, PALB2, RAD51C/D, or BARD1 . Pts were required to have metastasis, progressed on ≥1 regimen, and prior platinum-exposure unless refused/intolerant. Pts were treated with niraparib 200 mg orally once daily continuously. Dostarlimab 500 mg was administered IV every 3 weeks for 4 cycles, then 1000 mg IV every 6 weeks. The primary endpoint was disease control rate (DCR) at 12 weeks (DCR12) using iRECIST criteria. At least 8 pts with DCR12 in the first 19 eligible pts (41%) would be considered promising for further trials. Results: 22 pts were enrolled from Dec 2020 to Oct 2023. Two pts withdrew prior to receiving therapy, and 2 were later found to be ineligible, leaving 18 eligible pts for final analyses. Median age was 63.0 yrs, 39% male, 94% non-Hispanic white, 1 black (6%). 17 pts were platinum exposed, 7 were platinum-refractory, and 5 had previously progressed on PARPi maintenance. Mutation distribution was: BRCA2 13/18 (72%), BRCA1 4/18 (22%), BARD1 2/18 (11%). One pt had both BRCA1 (somatic) and BRCA2 (germline) mutations. Germline mutations were identified in 14/18 (78%). DCR12 was achieved in 5/18 (27.8%), so the primary endpoint (>41%) was not met. Pts received a median 2 cycles (range 1-8) of niraparib + dostarlimab. No clinical factors were significantly associated with improved DCR12 (all p > 0.05) in subgroup analyses, but we did observe the following statistically nonsignificant associations. Pts who were platinum-refractory had a lower DCR12 compared to those not (14.3% vs. 36.4%). Counterintuitively, PARPi exposed/refractory pts had a higher DCR12 than non-exposed/non-refractory (50 vs 17% exposed/nonexposed and 40% vs 23% refractory/nonrefractory). Pts with germline mutations had a higher DCR12 compared to those without (38.5% vs. 0%). Non- BRCA2 mutations had a higher DCR12 than those with BRCA2 mutations (50% vs.17%), with highest DCR12 rates among BRCA1 patients (67%). Tolerability was in line with similar combinations, with 60% non-hematologic Gr3 or higher, including fatigue (15%), AST increase (10%), nausea/vomiting (10%), sepsis (10%). Two grade 5 events were not attributed to treatment. Conclusions: PARPi plus anti-PD1 checkpoint inhibition was not sufficiently active as later line therapy in metastatic pancreatic cancer for the majority of patients with HRD mutations. Additional molecular analyses to elucidate predictive markers of sensitivity/resistance are ongoing. Clinical trial information: NCT04493060 .

Purpose: To investigate the molecular characteristics of and potential for precision medicine in KRAS wildtype pancreatic ductal adenocarcinoma (PDAC). Patients and Methods: We investigated 27 patients with KRASWT PDAC at our institution. Clinical data were obtained via chart review. Tumor specimens for each subject were interrogated for somatic single nucleotide variants, insertion and deletions, and copy number variants by DNA sequencing. Gene fusions were detected from RNA-seq. A patient-derived organoid (PDO) was developed from a patient with a MET translocation and expanded ex vivo to predict therapeutic sensitivity prior to enrollment in a phase 2 clinical trial. Results: Transcriptomic analysis showed our cohort may be stratified by the relative gene expression of the KRAS signaling cascade. The PDO derived from our patient harboring a TFG-MET rearrangement was found to have in vitro sensitivity to the multi-tyrosine kinase inhibitor crizotinib. The patient was enrolled in the phase 2 SPARTA clinical trial and received monotherapy with vebrelitinib, a c-MET inhibitor, and achieved a partial and durable response. Conclusions: KRASWT PDAC is molecularly distinct from KRASMUT and enriched with potentially actionable genetic variants. In our study, transcriptomic profiling revealed that the KRAS signaling cascade may play a key role in KRASWT PDAC. Our report of a KRASWT PDAC patient with TFG-MET rearrangement who responded to a cMET inhibitor further supports the pursuit of precision oncology in this sub-population. Identification of targetable mutations, perhaps through approaches like RNA-seq, can help enable precision-driven approaches to select optimal treatment based on tumor characteristics.

Abstract Deep learning may detect biologically important signals embedded in tumor morphologic features that confer distinct prognoses. Tumor morphologic features were quantified to enhance patient risk stratification within DNA mismatch repair (MMR) groups using deep learning. Using a quantitative segmentation algorithm (QuantCRC) that identifies 15 distinct morphologic features, we analyzed 402 resected stage III colon carcinomas [191 deficient (d)-MMR; 189 proficient (p)-MMR] from participants in a phase III trial of FOLFOX-based adjuvant chemotherapy. Results were validated in an independent cohort (176 d-MMR; 1,094 p-MMR). Association of morphologic features with clinicopathologic variables, MMR, KRAS, BRAFV600E, and time-to-recurrence (TTR) was determined. Multivariable Cox proportional hazards models were developed to predict TTR. Tumor morphologic features differed significantly by MMR status. Cancers with p-MMR had more immature desmoplastic stroma. Tumors with d-MMR had increased inflammatory stroma, epithelial tumor-infiltrating lymphocytes (TIL), high-grade histology, mucin, and signet ring cells. Stromal subtype did not differ by BRAFV600E or KRAS status. In p-MMR tumors, multivariable analysis identified tumor-stroma ratio (TSR) as the strongest feature associated with TTR [HRadj 2.02; 95% confidence interval (CI), 1.14–3.57; P = 0.018; 3-year recurrence: 40.2% vs. 20.4%; Q1 vs. Q2–4]. Among d-MMR tumors, extent of inflammatory stroma (continuous HRadj 0.98; 95% CI, 0.96–0.99; P = 0.028; 3-year recurrence: 13.3% vs. 33.4%, Q4 vs. Q1) and N stage were the most robust prognostically. Association of TSR with TTR was independently validated. In conclusion, QuantCRC can quantify morphologic differences within MMR groups in routine tumor sections to determine their relative contributions to patient prognosis, and may elucidate relevant pathophysiologic mechanisms driving prognosis. Significance: A deep learning algorithm can quantify tumor morphologic features that may reflect underlying mechanisms driving prognosis within MMR groups. TSR was the most robust morphologic feature associated with TTR in p-MMR colon cancers. Extent of inflammatory stroma and N stage were the strongest prognostic features in d-MMR tumors. TIL density was not independently prognostic in either MMR group.

AbstractIntroductionStructured data capture requires defined languages such as minimal Common Oncology Data Elements (mCODE). This pilot assessed the feasibility of capturing 5 mCODE categories (stage, disease status, performance status (PS), intent of therapy and intent to change therapy).MethodsA tool (SmartPhrase) using existing and custom structured data elements was Built to capture 4 data categories (disease status, PS, intent of therapy and intent to change therapy) typically documented as free‐text within notes. Existing functionality for stage was supported by the Build. Participant survey data, presence of data (per encounter), and time in chart were collected prior to go‐live and repeat timepoints. The anticipated outcome was capture of >50% sustained over time without undue burden.ResultsPre‐intervention (5‐weeks before go‐live), participants had 1390 encounters (1207 patients). The median percent capture across all participants was 32% for stage; no structured data was available for other categories pre‐intervention. During a 6‐month pilot with 14 participants across three sites, 4995 encounters (3071 patients) occurred. The median percent capture across all participants and all post‐intervention months increased to 64% for stage and 81%–82% for the other data categories post‐intervention. No increase in participant time in chart was noted. Participants reported that data were meaningful to capture.ConclusionsStructured data can be captured (1) in real‐time, (2) sustained over time without (3) undue provider burden using note‐based tools. Our system is expanding the pilot, with integration of these data into clinical decision support, practice dashboards and potential for clinical trial matching.