Saberzadeh-Ardestani, Bahar

Abstract Purpose: Immune checkpoint blockade (ICB) induces frequent and durable responses in metastatic deficient DNA mismatch repair (dMMR) colorectal cancer, yet substantial molecular heterogeneity and resistance remain. We sought to identify candidate tumor- and immune-related biomarkers associated with clinical outcomes following anti–PD-1 therapy. Experimental Design: Consecutive patients with metastatic dMMR colorectal cancer (N = 39) treated with anti–PD-1 therapy underwent tumor profiling using a validated immune-enhanced exome and transcriptome platform. Microsatellite instability (MSI) burden was quantified as the percentage of unstable microsatellite loci using MSIsensor-pro. Associations with objective response were evaluated, and progression-free survival (PFS) and overall survival (OS) were analyzed using Cox proportional hazards models. Results: Higher MSI burden was associated with improved objective response (P = 0.018) and survival. The dichotomized MSI level (Q2–4 vs. Q1) was associated with longer PFS [hazard ratio (HR), 0.18; 95% confidence interval (CI), 0.06–0.56; P = 0.003] and OS (HR, 0.20; 95% CI, 0.07–0.58; P = 0.003), with similar results when modeled continuously. MSI burden correlated with neoantigen clonality but not burden (R = 0.53, P = 0.01). Responders exhibited significantly greater T-cell receptor repertoire diversity; both T- and B-cell receptor diversities were associated with survival. Although human leukocyte antigen (HLA) A, HLA-B, and HLA-C expression was not prognostic, the HLA-B*07:02 allele was associated with the best overall response. In contrast, overexpression of immune exhaustion–related genes and cytotoxic T-cell and natural killer cell exhaustion phenotypes were associated with ICB resistance and poorer prognosis. Conclusions: Integrated exome/transcriptome profiling with MSI quantification identified MSI burden and adaptive immune repertoire diversity as potential correlates of response and survival following ICB. These findings suggest a mechanistic link among genomic instability, antigen recognition diversity, and immunotherapy benefit.

PURPOSE The development of colorectal liver metastases (CRLMs) is associated with poor prognosis, and recent data suggest that metastasis to the liver is associated with resistance to immunotherapy. We characterized the microenvironment of primary colorectal carcinomas (CRCs) relative to their synchronous CRLMs using a validated segmentation algorithm (QuantCRC) that quantifies 15 distinct morphologic tumor features. MATERIALS AND METHODS Adult patients with CRC with synchronous CRLM (N = 57) at Mayo Clinic were identified from the electronic health record. Routine tumor hematoxylin and eosin sections were digitized and reviewed for quality control. QuantCRC (Aiforia, Inc) was applied to digitized images to extract 15 GI pathologist predefined morphological features. Tumor features were compared between paired primary tumors and their synchronous CRLMs using the Wilcoxon signed-rank test overall and within subgroups. Linear regression models were used to find predictors of the paired differences in the distinct tumor morphological features. RESULTS The study included 57 patients (median age 59 years [IQR, 50-73], 51% female) with CRC primary and synchronous CRLM, and among primaries, 37 (65%) were left-sided. QuantCRC identified six of 15 morphological features that differed significantly between primaries and their CRLMs. Compared with the primary, CRLM showed reduced stroma, more high-grade tumor and necrosis, higher tumor–stromal ratio (TSR; all P < .05), and a trend toward increased tumor-infiltrating lymphocyte (TIL) density ( P = .052). Synchronous CRLM from patients with left-sided primary tumors had significantly higher TSR, percent high-grade, percent necrosis, and TIL density compared with the primary (all P values ≤0.04). Among right-sided primaries, CRLM had a significantly reduced percent mature stroma ( P = .0296) and a higher percent necrosis ( P = .0027). CONCLUSION Compared with the primary, synchronous CRLMs showed a higher TSR and increased high-grade tumor and necrosis, which are features of tumor aggressiveness and a metastatic phenotype.

Abstract Purpose: Digital proteomic profiling was performed to identify spatial context in relationship to patient response and survival after anti–PD-1 therapy in metastatic colorectal cancer. Experimental Design: Primary colorectal cancers with deficient mismatch repair from patients treated with anti–PD-1 antibodies were analyzed (N = 30) using digital spatial profiling (GeoMx nCounter). At the invasive margin, 71 proteins were profiled in 10 regions of interest/slide that were segmented into 3 compartments labeled with pan-cytokeratin (epithelia), CD45 (stromal cells), and SYTO13 (nuclei). In an independent cohort (n = 13), digital spatial profiling data and single-cell transcriptomic data were analyzed. Differential protein abundance, after Benjamini–Hochberg correction, was examined by clinical response and progression-free survival (PFS) using multivariable Cox regression. Results: Protein abundance varied significantly between epithelial and stromal compartments. Nonresponders to anti–PD-1 showed higher fibronectin and smooth muscle actin abundance in the epithelial compartment that was associated with significantly shorter PFS (adjusted HR: 6.49 and 4.52, respectively; P &lt; 0.05). In CD45+ stroma, increased expression of proteins related to T cells (CD3 and CD4), NK cells (CD56), antigen presentation (CD40), immune activation (CD27, ICOS), and apoptosis (GZMA) were found in responders (vs nonresponders) to anti–PD-1; each marker was significantly associated with longer patient PFS (0.02 &lt; adjusted HR &lt; 0.17; P &lt; 0.05). In a separate cohort, consistent results by compartment were found for fibronectin and CD56. Gene expression data revealed that fibronectin and smooth muscle actin were primarily derived from cancer-associated fibroblasts. Conclusions: Spatially resolved protein profiles within microenvironments of deficient mismatch repair colorectal cancers can influence patient response and survival after anti–PD-1, highlighting their potential clinical significance.

270 Background: While immune checkpoint blockade (ICB) is used to treat metastatic deficient mismatch repair (dMMR) colorectal cancers (CRCs), such tumors are heterogeneous and resistance to ICB is frequent. To date, biomarkers that can consistently predict patient response to ICB are lacking. We determined whether biomarkers that integrate tumor and immune-related molecular mechanisms could predict ICB response. Methods: Consecutive patients with metastatic dMMR CRCs (N=32) who had been treated with anti-PD-1 therapy were profiled using a validated immune-enhanced exome and transcriptome platform (ImmunoID NeXT, Personalis) using tumor and paired normal tissue. This platform provides antigen-specificity of T or B cells, indicated by the TCR or BCR respectively, neoantigen prediction, tumor mutation burden (TMB), immune profile, HLA allele-specific loss of heterogeneity (LOH) and a Neoantigen Presentation Score (NEOPS) that combines a neoantigen prediction tool with mechanisms of immune evasion. Tumor variables were analyzed in relationship to objective tumor response, and a Cox proportional hazards model was fit to predict progression-free survival (PFS) or overall survival (OS). Results: In CRCs, TCR and BCR repertoire diversity were each significantly increased in tumors from responders versus those with stable or progressive disease after anti-PD-1 treatment (p=0.015 and p=0.048, respectively). Neoantigen burden score, TMB, and HLA allele-specific LOH (reflects loss of antigen presentation machinery) were each unable to distinguish responders from nonresponders. An immune checkpoint gene expression signature was increased in nonresponders relative to responders (P= 0.005). Neoantigen presentation score (NEOPS) was prognostic when adjusted for tumor grade only for OS (p=0.048). Conclusions: The T-cell and B-cell receptor (TCR/BCR) repertoire, as well as NEOPS, were each significantly predictive of favorable response to anti-PD-1 therapy in dMMR tumors. In addition, an immune checkpoint gene expression signature was predictive of resistance to ICB.

251 Background: The development of colorectal liver metastases (CRLM) is associated with poor prognosis, and recent data suggest that metastasis to the liver is associated with resistance to immunotherapy. We characterized the microenvironment of primary colorectal carcinomas (CRCs) relative to their synchronous CRLM using a validated segmentation algorithm that quantifies 15 distinct morphologic tumor features. Methods: Adult CRC patients with synchronous CRLM (N=57) at Mayo Clinic were identified from the electronic health record using Epic Slicer Dicer and from an internal database. Tumor H&E sections were digitized and reviewed for quality control (RKP). QuantCRC (Aiforia, Inc) was applied to digitized images to extract 15 GI pathologist pre-defined morphological features. Tumor features were compared between primaries and CRLM using the Kruskal–Wallis test. The project was approved by the Mayo Clinic Institutional Review Board. Results: The study included 57 patients (median age 59 years [IQR 50, 73], 51% female) with CRC primaries and synchronous CRLM. Among primaries, 20 (35%) were right-sided and 37 (65%) were left-sided. QuantCRC identified 6 of 15 morphological features that differed significantly between primaries and their CRLM, including reduced stroma, more high-grade and necrosis, and a higher tumor: stromal ratio (TSR) in CRLM (Table). The increase in TIL density in CRLM vs the primary tumor was of borderline significance (p =0.053). Among patients with left-sided primary tumors, their CRLM had significantly higher TSR, percent high-grade, percent necrosis, and TIL density compared to the primary (all p values ≤ 0.02). Among right sided primaries, CRLM had a significantly reduced percent mature stroma (p=0.034) whereas percent necrosis was increased (p =0.01). Conclusions: Using deep learning, we identified tumor morphological features that differed significantly between primary CRC and their synchronous CRLM. This included higher TSR in CRLMs compared to primaries that is associated with epithelial-mesenchymal transition and has been shown to contribute to treatment resistance. Analysis of tumor morphological features with patient prognosis is ongoing. Deep learning-derived morphological features of primary CRC and CRLM. Morphological feature (Median) Primary tumor Liver metastasis P-value Tumor-Stroma Ratio 0.78 1.50 0.008 % High-grade 23.08 31.72 0.005 TIL Count 44.36 60.20 0.053 % Necrosis 6.35 19.32 <0.001 %Signet Ring Cells 0.25 0.13 0.088 TB/PDC 0.89 1.13 0.484 % Stroma 54.89 40.98 <0.001 % Immature Stroma 46.76 34.13 <0.001 % Mature Stroma 6.27 5.08 0.030

Abstract Purpose This study examines sense of belonging (belongingness) in a large population of medical students, residents, and fellows and associations with learner burnout, organizational recruitment retention indicators, and potentially modifiable learning environment factors. Method All medical students, residents, and fellows at Mayo Clinic sites were surveyed between October and November 2020 with items measuring sense of belonging in 3 contexts (school or program, organization, surrounding community), burnout (2 Maslach Burnout Inventory items), recruitment retention indicators (likelihood of recommending the organization and accepting a job offer), potentially modifiable learning environment factors, and demographics (age, gender, race and ethnicity, LGBTQ+ identification, disability, socioeconomic background). Results Of 2,257 learners surveyed, 1,261 (56%) responded. The percentage of learners reporting a somewhat or very strong sense of belonging was highest in the school or program (994 of 1,227 [81%]) followed by the organization (957 of 1,222 [78%]) and surrounding community (728 of 1,203 [61%]). In adjusted analyses, learners with very strong organization belongingness had lower odds of burnout (odds ratio [OR], 0.05; 95% CI, 0.02–0.12) and higher odds of being likely to recommend the organization (OR, 505.23; 95% CI, 121.54–2,100.18) and accept a job offer (OR, 38.68; 95% CI, 15.72–95.15; all P &lt; .001). School or program and community belongingness also correlated strongly with these outcomes. In multivariable analyses, social support remained associated with higher odds of belongingness in all 3 contexts; favorable ratings of faculty relationships and leadership representation remained associated with higher odds of belongingness in 2 contexts (school or program and organization); and favorable ratings of diversity, equity, and inclusion learning climate remained associated with belongingness in 1 context (community). Conclusions Sense of belonging among medical students, residents, and fellows varies across contexts, correlates strongly with burnout and organizational recruitment retention indicators, and is associated with multiple potentially modifiable learning environment factors.

Abstract Background Tumor microenvironment (TME) characteristics including tumor stroma ratio (TSR), tumor budding (TB), and tumor-infiltrating lymphocytes (TILs) were examined in resected gastric cancer. These TME features have been shown to indicate metastatic potential in colon cancer, and intestinal-type gastric cancer (IGC) has pathological similarities with that malignancy. Methods TSR, TB, and TILs were quantified in routine histological sections from 493 patients with IGC who underwent radical resection at 2 university hospitals in China from 2010 to 2016. TME variables were dichotomized as follows: TSR (50%), TILs (median), TB per international guidelines (4 buds/0.785mm2), and platelet-lymphocyte ratio (PLR) per survival ROC. Association of TME features with patient clinicopathological characteristics, time-to-recurrence (TTR), and cancer-specific-survival (CSS) were examined using univariate and multivariate analysis, including a relative contribution analysis by Cox regression. Results Patients whose tumors showed high TSR or high TB or low TILs were each significantly associated with increased T and N stage, higher histological grade, and poorer TTR and CSS at 5 years. Only TSR and N stage were independently associated with TTR and CSS after adjustment for covariates. PLR was only independently associated with TTR after adjustment for covariates. Among the variables examined, only TSR was significantly associated with both TTR (HR 1.72, 95% CI, 1.14-2.60, P = .01) and CSS (HR 1.62, 95% CI, 1.05-2.51, P = .03) multivariately. Relative contribution to TTR revealed that the top 3 contributors were N stage (45.1%), TSR (22.5%), and PLR (12.9%), while the top 3 contributors to CSS were N stage (59.9%), TSR (14.7%), and PLR (10.9%). Conclusions Among the examined TME features, TSR was the most robust for prognostication and was significantly associated with both TTR and CSS. Furthermore, the relative contribution of TSR to patient TTR and CSS was second only to nodal status.

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.

Abstract Background: Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of patients with deficient DNA mismatch repair (d-MMR) cancers, yet less than half of patients with metastatic colorectal cancer (mCRC) receive clinical benefit. We analyzed heterogeneity within d-MMR tumors using a multi-omics approach inclusive of percent unstable microsatellite loci (MS). Methods: We studied consecutive patients with d-MMR mCRC with available primary tumors (N=32) that were treated with an anti-PD-1 antibody at Mayo Clinic (2015-2022). Tumors were profiled using a whole exome and transcriptome platform (ImmunoID NeXT®). MSIsensor was used to calculate percent unstable microsatellite loci from whole exome sequencing data. CIBERSORT and ssGSEA were used to analyze immune gene expression in relationship to percent unstable microsatellite loci. An immune checkpoint gene expression signature was calculated as the mean expression of CD274, CTLA-4, HAVCR2, LAG-3, PDCD1, PDCD1LG2, TIGIT. Aforementioned variables were analyzed in relationship to objective tumor response (RECIST version 1.1). A Cox proportional hazards model was fit to predict progression-free survival (PFS). Results: Among 32 patients with d-MMR mCRC treated with PD-1 blockade, 16 (50%) received first-line anti-PD-1 therapy; others had &gt; 1 prior chemotherapy regimen. Eighteen (56.3%) tumors harbored BRAFV600E, 4 (12.5%) had mutant KRAS, and median PFS was 24.6 months (95% CI: 8.4, NR) with 18 events. Neither tumor mutation burden (TMB) nor PD-L1 mRNA expression predicted PFS. Median percent unstable MS loci was 14.38 (IQR: 2.6-23.2). Patients whose tumors had higher percent unstable MS loci had significantly better PFS (Q2-4 vs Q1; HR: 0.22, 95%CI: 0.06, 0.62; P=0.003), and were associated with increased objective response (P=0.049). Tumors with higher unstable loci also had higher neoantigen clonality (R= 0.64; P= 0.0003) and increased M0 (resting) macrophages (P=0.02). Nonresponders (vs responders) to PD-1 blockade had an increased immune checkpoint expression signature (P= 0.005). Conclusion: A higher percentage of unstable microsatellite loci, associated with higher neoantigen clonality and resting macrophages, was predictive of anti-PD-1 efficacy. Furthermore, an immune checkpoint gene expression signature was associated with objective response. Citation Format: Bahar Saberzadeh-Ardestani, Oluwadunni E. Emiloju, Rondell P. Graham, Jason T. Lewis, Charles W. Abbott, Sean M. Boyle, Frank A. Sinicrope. Prediction of PD-1 treatment outcome in mismatch repair deficient metastatic colorectal cancer using a multi-omic approach [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 578.