Citations

AbstractAimThe relationship between the number of stapler firings (SFs) during robotic rectal transection and the risk of colorectal/coloanal anastomotic leakage (AL) is controversial. This study investigates whether the number of robotic SFs is associated with an increased risk of AL.MethodRetrospective review of adult patients who underwent robotic total mesorectal excision (TME) and partial mesorectal excision (PME) with double‐stapled colorectal or coloanal anastomosis for both benign and malignant colorectal diseases between 2015 and 2023 across three enterprise‐wide hospitals. Primary outcome: the effect of multiple SFs on postoperative AL rate among patients who underwent one, two, or three or more SFs. Secondary outcome: the effects of other potential risk factors on postoperative AL.ResultsOf 503 patients who underwent robotic colorectal resection with a double‐stapled anastomosis, 56 (11%) developed a postoperative AL. The number of SFs was not associated with the AL rate (p = 0.51): the univariable and multivariate analyses found no correlation between the AL rate and the increasing number of SFs required, compared with one SF as the reference (adjusted two SFs, OR = 1.1, 95% CI: 0.6–2.2, p = 0.77 vs. adjusted three or more SFs, OR = 0.98, 95% CI: 0.4–2.3, p = 0.96). On multivariate analysis, AL was strongly associated with male gender (OR = 2.5, 95% CI: 1.3–4.9, p = 0.005) and with TME versus PME (OR = 2.8, 95% CI: 1.5–5.5, p = 0.002).ConclusionThe number of robotic SFs for rectal transection is not correlated with postoperative AL.

Abstract Clonal hematopoiesis (CH) of indeterminate potential (CHIP), driven by somatic mutations in leukemia-associated genes, confers increased risk of hematologic malignancies, cardiovascular disease, and all-cause mortality. In blood of healthy individuals, small CH clones can expand over time to reach 2% variant allele frequency (VAF), the current threshold for CHIP. Nevertheless, reliable detection of low-VAF CHIP mutations is challenging, often relying on deep targeted sequencing. Here, we present UNISOM, a streamlined workflow for enhancing CHIP detection from whole-genome and whole-exome sequencing data that are underpowered, especially for low VAFs. UNISOM utilizes a meta-caller for variant detection, in couple with machine learning models which classify variants into CHIP, germline, and artifact. In whole-exome sequencing data, UNISOM recovered nearly 80% of the CHIP mutations identified via deep targeted sequencing in the same cohort. Applied to whole-genome sequencing data from Mayo Clinic Biobank, it recapitulated the patterns previously established in much larger cohorts, including the most frequently mutated CHIP genes and predominant mutation types and signatures, as well as strong associations of CHIP with age and smoking status. Notably, 30% of the identified CHIP mutations had < 5% VAFs, demonstrating its high sensitivity toward small mutant clones. This workflow is applicable to CHIP screening in population genomic studies. The UNISOM pipeline is freely available at https://github.com/shulanmayo/UNISOM and https://ngdc.cncb.ac.cn/biocode/tool/7816.

Abstract DAC combinations serve as frontline therapies for AML. DAC forms DNA-DNMT1 crosslinks and activates the ATR-CHK1-WEE1 DDR. Combinations of DNA-protein crosslinking agents with ATR, CHK1, or WEE1 inhibitors (i) have shown efficacy in solid tumors. This study evaluates the cytotoxic synergy of DAC plus ATRi, CHK1i, or WEE1i in AML. Public datasets were queried for mRNA expression and dependency. Commercial cell lines (U937, KG1a, Molm13, MV-4-11) were genomically validated. Molm13 TP53-/- clones were generated via CRISPR. Cells were cultured in complete RPMI. To treat, cells were cultured for 48 h in RPMI with drugs or diluent (0.2% v/v DMSO). Protein expression was evaluated by Western blotting (WB). Apoptosis was assayed by flow cytometry with annexin V or propidium iodide. Primary AML samples were obtained at study centers after informed consent. Isolated mononuclear cells (Ficoll) were used in colony forming unit (CFU) assays with DMSO or drug for 12-16 days. Synergy was determined by median-effect (CalcuSyn) where a combination index <1 is synergistic and >1 antagonistic. DNMT1 expression (TCGA) was highest in AML vs all other tumor types (p<0.001). In CRISPR screens (DepMap), AML cell lines were increasingly dependent on DNMT1, ATR, CHK1, and WEE1 (ANOVA p<0.001). WB of AML cell lines treated with DAC (25 - 200 nM) for 24 h showed phosphorylations of H2AX and CHK1 S296/S345 that were most pronounced in TP53MT lines. In apoptosis assays, ATRi, CHK1i, and WEE1i were synergistic with clinical concentrations of DAC (Table 1). In isogenic Molm13 lines, TP53 loss conferred DAC resistance that was overcome with combination treatment. Selective CHK1i were generally more synergistic than CHK1/2i, consistent with a CHK1 DDR. CFU assays of primary AML samples also showed synergy between DAC and ATRi, CHK1i, and especially WEE1i. Table 1. Synergy between decitabine and replication checkpoint inhibitors in preclinical models of AML. Cell line data are derived from subdiploid apoptosis assays. U937 data were replicated using annexin V. Prexasertib showed no activity in KG1a cells (suggesting drug efflux) requiring the use of the alternate CHK1 inhibitor, rabusertib. Abbreviations: ATR, ataxia telangiectasia and Rad3 related; CHK1/2, checkpoint kinase 1 and/or 2; CI, combination index; Conc, concentration; DNMT1, DNA methylatransferase 1; IQR, interquartile range; MT, mutated; ND, not determined; SD, standard deviation; WT, wild type. Drug Target Conc. (nM) Molm13 (TP53WT) Molm13 (TP53-/-) U937 (TP53G187fs*/-) KG1a (TP53V225fs*/-) Primary AML (TP53WT orTP53MT) Units Decitabine DNMT1 6.25 – 200 25 ± 11 350 ± 260 310 ± 180 6.1 ± 1.6 ND EC50, nM (mean, SD) Ceralasertib ATR 250 – 500 0.93 (0.66 – 1.24) 0.90 (0.84 – 0.96) 0.15 (0.08 – 0.21) 0.74 (0.63 – 1.02) 0.66 (0.43 – 2.43) CI with decitabine (median, IQR) Camonsertib ATR 10 – 100 0.27 (0.19 – 0.59) 0.24 (0.22 – 0.34) 0.34 (0.21 – 0.39) ND ND Prexasertib CHK1/2 1.0 – 6.0 0.89 (0.82 – 1.06) 0.78 (0.68 – 0.90) 0.95 (0.93 – 1.08) ND 0.88 (0.29 – 1.28) MK-8776 CHK1 250 – 1, 000 0.57 (0.48 – 0.80) 0.76 (0.73 – 1.19) 0.89 (0.57 – 1.39) ND ND Rabusertib CHK1 600 – 1, 000 ND ND ND 0.51 (0.48 – 0.56) ND Adavosertib WEE1 100 – 400 0.70 (0.63 – 1.11) 0.99 (0.78 – 1.14) 0.67 (0.60 – 0.74) 0.67 (0.37 – 0.93) 0.65 (0.43 – 0.88) DAC synergizes with ATRi, CHK1i, and WEE1i in AML. Future work will test these combinations in vivo and incorporate BH3 mimetics. Citation Format: Clifford M. Csizmar, Antoine N. Saliba, Olivia K. Rossman, Kevin L. Peterson, X. Wei Meng, B. Douglas Smith, Gabriel Ghiaur, Jonathan A. Webster, Mrinal M. Patnaik, Aref Al-Kali, Scott H. Kaufmann. Decitabine (DAC) induces a DNA damage response (DDR) and synergizes with replication checkpoint inhibitors in acute myeloid leukemia (AML) [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 1170.

Abstract Background: Replication checkpoint modulators (RCMs), including inhibitors of CHK1, ATR, and WEE1, have been widely studied for their ability to inhibit cell cycle checkpoints and enhance the toxicity of agents that stall replication forks. In preclinical studies each of these inhibitors also demonstrates notable activity as a single agent against relapsed/refractory acute myeloid leukemia (R/R AML). In particular, our prior study (PMID: 33414171) demonstrated that several myeloid cell lines underwent cell death at nanomolar concentrations, with a median IC50 of 9 nM irrespective of the TP53 status. Examination of pathways involved in RCM-induced leukemia cell killing demonstrated that CHK1 inhibition induces CDC25A upregulation accompanied by activation of a death program that involves a CDK2-initiated 10- to 50-fold upregulation of TNFα and downstream engagement of the extrinsic apoptotic pathway. Here we have comprehensively characterized the processes that occur between the interruption of replication checkpoints and leukemic cell death in these sensitive cells versus cells selected for RCM resistance. Methods: Using the CHK1 inhibitors MK8776 and prexasertib, ATR inhibitors berzosertib and ceralasertib, and WEE1 inhibitor adavosertib as paradigm drugs, we examined signaling in two AML cell lines, U937 and THP.1, that were selected for CHK1 inhibitor resistance, examined cross-resistance patterns, and assessed the biochemical basis for leukemic cell death. Changes in gene expression and signaling induced by RCMs were analyzed in isogenic pairs using RNA sequencing, flow cytometry and immunoblotting. Results: In contrast to parental U937 and THP.1 cells, CHK1 inhibitor-selected U937 and THP.1 cell exhibited a lack of TNFα mRNA induction and did not undergo cell death in response to multiple CHK1 inhibitors. RNA sequencing of CHK1 inhibitor-selected AML lines revealed an almost complete absence of induction of CDK2-activated genes following prexasertib treatment. Interestingly, unlike solid tumors, AML cell lines that were resistant to CHK1 inhibitors exhibited hypersensitivity to WEE1 or ATR inhibition. Further studies demonstrated that CHK1 inhibitor response was varied in primary AML samples, with 50% of primary AML specimens resistant to CHK1i monotherapy, again without any relationship to TP53 status. The common features among sensitive vs. resistant leukemias was the successful inhibition of CDK2 phosphorylation and the associated induction of TNFα. Conclusions: Our findings reveals critical new insights into the factors that influence the response to CHK1 inhibitors in AML. Importantly, we found no evidence of cross-resistance to inhibitors of other replication checkpoint proteins, enabling alternative options. Citation Format: Annapoorna Venkatachalam, Husheng Ding, Nicole Vincelette, Cordelia McGehee, Kevin Peterson, Paula Schneider, Cristina Correia, Hu Li, Scott Kaufmann. CDK2 activation mediates response of acute myeloid leukemia to CHK1, ATR and WEE1 inhibitors [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 423.

Study Design: Analysis of prospectively collected data. Objective: To assess if systemic illness severity affects cervical spondylotic myelopathy (CSM) surgery outcomes. Summary of Background Data: It remains unclear if CSM patients with poor physical status/severe systemic illness benefit as much from surgery as those in good condition. Methods: Using the Quality Outcomes Database CSM cohort and the American Association of Anesthesiology (ASA) grade as a surrogate for illness burden, we compared patients with (ASA 3–4) and without (ASA 1–2) severe systemic illness, including rates of readmission and 24-month minimal clinically important differences (MCID) achievement for patient-reported outcomes (PROs)—numerical rating score (NRS) arm and neck pain, neck pain-related disability (NDI), and quality of life (EQ-5D). Results: Of 1141 CSM patients, 1062 had ASA grades recorded. Of these 1062 patients, 70.2% had a 2-year follow-up for mJOA, and 81%–84% had a follow-up for NRS arm and neck, NDI, and EQ-5D. Five hundred twenty-one patients (49.1%) had mild (ASA 1–2) and 541 (50.9%) had severe systemic illness (ASA 3–4). The severe disease cohort was older (63.3±11.0 vs. 57.4±11.7), had higher BMI (31.4±7.0 vs. 28.9±5.6), had more comorbidities (diabetes, coronary artery disease, depression), and had less independent ambulation (71.3% vs. 90.6%) ( P <0.05). At baseline, severe disease patients had worse NRS arm (5.2±3.5 vs. 4.7±3.4) and neck (5.5±3.2 vs. 5.1±3.3) pain, NDI (40.5±20.1 vs. 36.8±21.0), and EQ-5D (0.53±0.22 vs. 0.59±0.22) scores ( P <0.05). Perioperatively, the severe disease cohort had longer hospitalizations (2.4±2.6 vs. 1.7±2.0 days) and increased nonhome discharges (17% vs. 5%) ( P <0.05). The severe disease cohort had higher 90-day readmissions (7.6% vs. 2.5%), including surgery-related (3.7% vs. 1.5%) and non–surgery-related reasons (3.9% vs. 1.0%) ( P <0.05). On multivariate analysis, increased ASA grade was significantly associated with 90-day readmissions (OR: 2.55 per 1-grade increase, 95% CI: 1.38–4.83). However, both severe and mild disease cohorts had similarly high rates of achieving 2-year MCID for mJOA (67.5% vs. 66.0%), NRS arm (72.0% vs. 74.1%), neck (69.5% vs. 69.4%) pain, NDI (63.1% vs. 68.1%), and EQ-5D (67.9% vs. 66.9%) ( P >0.05). Conclusion: Patients with severe systemic illness (higher ASA) have worse baseline PROs and higher 90-day readmissions. However, they achieve similar MCID rates for mJOA and all measured PROs 2 years postoperatively.

Pathology remains a labor-intensive discipline, relying on workflows rooted in practices established over a century ago. Despite incremental technological advancements, the diagnostic process—from tissue preparation to interpretation—still heavily depends on thinly sliced tissue sections stained and examined under brightfield microscopy by skilled pathologists. Difficult cases often necessitate iterative staining or complex immunohistochemistry (IHC) analyses. However, the recent emergence of machine learning (ML) presents an opportunity to revolutionize traditional diagnostic workflows. One promising advancement is Digital Chemical Pathology (DCP), a paradigm that combines modern ML algorithms with infrared (IR) imaging to streamline pathology and enhance diagnostic accuracy. Infrared wavelengths inherently align with molecular vibrational frequencies, providing intrinsic contrast based on the chemical composition of samples. Mid-IR spectroscopic microscopes leverage this principle to spatially resolve molecular distributions at the pixel level, creating "chemical fingerprints" that ML algorithms can correlate with cellular identities and disease states. Recent advancements, particularly the development of broadly tunable quantum cascade lasers (QCLs), have enabled faster IR imaging, paving the way for clinical applications of IR spectroscopic microscopes. In this research, we demonstrate the potential of DCP using frozen tissue samples. Our approach focuses on two core tasks: (1) virtual staining, translating IR images into realistic, stained images interpretable by pathologists, and (2) tissue segmentation, enabling the automated detection of cancer and the distinction between normal and cancerous tissues. Furthermore, we incorporate statistical safety algorithms to generate uncertainty maps, ensuring the reliability of predictions. Our findings highlight that the integration of IR imaging and ML can facilitate automated diagnostics, providing pathologists with powerful tools for enhanced healthcare delivery. By leveraging DCP, we aim to modernize pathology workflows, reduce diagnostic turnaround times, and improve patient outcomes through precise and efficient analysis. Citation Format: Kianoush Falahkheirkhah, Kevin Yeh, Ji-Hun Oh, Yen-Ting Liu, Ruo-Jing Ho, Ishaan Sharma, Matthew Confer, Andres Orr, Mykolas Tamonis, John Cheville, Rohit Bhargava. Transforming cancer diagnosis with digital chemical pathology [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 2470.

Abstract Deep learning has advanced digital pathology and multi-omics data analysis. However, there is still a critical need to integrate these modalities to better understand complex diseases. Deep learning models, such as Prov-Gigapath, a state-of-the-art pathology foundation model, have been developed for analyzing whole-slide images (WSI). Our previously published deep-learning framework, OmicsFootPrint (OFP), transforms high-dimensional multi-omics data into intuitive genomically-arranged circular images and utilizes transfer learning to overcome the challenges of high-dimensional, low-sample-size data. In this study, we aimed to combine WSI and multi-omics data to develop a framework capable of distinguishing between invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC), which is clinically significant as it may help explain differential responses to therapies, such as tamoxifen versus letrozole. We enhanced the performance of our OFP model by dividing each multi-omics circular image (1024x1024x3 pixels) into 16 equal-sized patches (256x256x3 pixels). Each patch was trained using a separate EfficientNetV2 architecture with pre-trained ImageNet weights. The probability outcomes from these 16 models were subsequently combined through a meta-learner model using the AutoGluon framework. Concurrently, WSI images corresponding to the same patients were independently trained, maintaining consistent train/validation/test splits. The Prov-Gigapath model, which was trained using a dataset from the Providence Health System, was employed to extract patch-level embeddings from the WSI data. These embeddings were fine-tuned using a LongNet-based masked autoencoder to predict outcomes. Finally, predictions from the OFP model and the WSI-based Prov-Gigapath model were integrated through a meta-learner, preserving the original train/validation/test split. Model performances (AUC) were evaluated using the held-out test set. The gold standard used for the analysis was histological identification of IDC versus ILC, against which the performance of omics data alone, WSI data alone, and the multi-modal integration approach were compared. We evaluated our integrated model as a proof-of-concept using The Cancer Genome Atlas (TCGA) breast cancer cohort, comprising 92 ILC and 391 IDC samples. Multi-omics data, including gene expression, microRNA, copy number variation, and RPPA, were analyzed using the OFP framework, while WSI data were processed using the Prov-Gigapath method. The results demonstrated that the multi-modal integration approach significantly outperformed individual modalities, achieving an AUC of 0.98 compared to 0.87 for OFP (original), 0.95 for OFP (16-patches), and 0.92 for WSI. Integrating WSI and multi-omics data improves predictive accuracy, significantly improving the classification of breast cancer subtypes over histology alone. Citation Format: Naresh Prodduturi, Xiaojia Tang, Hamid R. Tizhoosh, Kevin J. Thompson, Richard Weinshilboum, Karthik V. Giridhar, Judy C. Boughey, Eric W. Klee, Liewei Wang, Matthew P. Goetz, Vera Suman, Krishna R. Kalari. A multi-modal learning framework to integrate digital pathology image and multi-omics data in breast cancer [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 2429.