Cancer Grade Research Articles

Abstract 6211: Clinical and prognostic assessment of the lncRNA ZFAS1-related gene signature in colorectal cancer

Abstract Colorectal cancer (CRC) is one of the leading causes of cancer death with a rapidly rising incidence rate in younger adults. It is projected to be the leading cause of death in adults under 50 years old by 2030. Although progress over the last couple of decades has improved the clinical management of patients affected by CRC, there is a lack of accurate biomarkers to stratify CRC patients. Among several other biomolecules, lncRNAs (long non-coding RNAs) are non-coding transcripts under 200 nucleotides that can regulate gene expression. Furthermore, lncRNAs are important players in oncogenesis and cancer progression and have the potential to be used as reliable biomarkers. Through this study, we explored the clinical relevance of lncRNAs in colorectal cancer. To analyze the distribution of lncRNAs, we utilized de-identified FFPE (formalin-fixed paraffin-embedded) blocks from Augusta University (n = 88). FFPE blocks were sectioned to extract tissue segments and were subsequently stained using H&E (hematoxylin and eosin dyes) to identify the tumor and adjacent normal regions. The quantification of the gene expression was performed using a medium-throughput NanoString platform. Further analyses were performed using clinicopathological features of the institutional cohort and an independent Cancer Genome Atlas (TCGA) dataset. TCGA contains genomic data along with clinical and molecular characterization of over 11,000 samples of various lineages. In the institutional cohort, ZFAS1 showed higher gene expression in the low survival group for stage II and III colorectal cancer patients, while no significant difference was observed in stage I and IV patients. ZFAS1 expression did not significantly correlate with other clinicopathological parameters, including race, alcohol consumption, tobacco consumption, age, grade, and family history of cancer. In an orthogonal TCGA analysis, higher ZFAS1 expression was associated with genomic perturbations, including significantly altered tumor mutational burden (TMB), aneuploidy, fractional genome alteration, and microsatellite instability. Further, a 21-gene signature was developed for prognostication using gene co-expression network analysis. The gene signature was significantly associated with overall and disease-free survival in the TCGA-CRC cohort. Immune deconvolution analysis of the RNA sequencing data revealed that higher expression of the 21-gene signature was associated with increased infiltration of regulatory T cells while being inversely correlated with the overall level of T cells. In conclusion, our study identified the clinicopathological role of ZFAS1 lncRNA and its interaction genes with prognostic significance in colorectal cancer. Further investigation into the therapeutic relevance of the ZFAS1-related gene signature may prove invaluable in improving the clinical outcomes of colorectal cancer patients. Citation Format: Pankaj Kumar Ahluwalia, Tiffanie Leeman, Ashis Mondal, Ashutosh Vashisht, Harmanpreet Singh, Nivin Omar, Kimya Jones, Ravindra Kolhe. Clinical and prognostic assessment of the lncRNA ZFAS1-related gene signature in colorectal cancer [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 6211.

Abstract 3486: Triple hormone receptor signatures as novel prognostic markers in breast cancer

Abstract Background: Tumor phenotype across various cancers, including breast cancer, is predominantly shaped by a synergy between genomic alterations and the cell-of-origin from which the tumor originates. These elements collectively influence crucial aspects like tumor aggressiveness, treatment response, and patient prognosis. While existing research highlights that the role of cell-of-origin in shaping the molecular architecture of breast cancer, leveraging this information for actionable clinical insights has proven challenging. Here, we introduce two robust models leveraging cellular ancestry signatures of breast luminal cells to improve patient stratification and outcome prediction. Methods: In this study, we developed a unique gene signature anchored in the expression levels of triple hormone receptors (THR)—namely androgen (AR), estrogen (ER), and vitamin D (VDR)—in breast cancer cells. Building on this, we formulated two distinct mRNA markers, THR-50 and THR-70, aimed at categorizing breast tumors based on their THR expression profiles. These markers were rigorously validated across 65 independent breast cancer studies, involving a total of 6,679 patients, utilizing Kaplan-Meier survival curves, Cox proportional hazards models, and unsupervised clustering analyses. Results: Our findings indicate that both THR-50 and THR-70 are robustly associated with overall survival and recurrence-free survival in breast cancer patients across all datasets evaluated. Importantly, these THR signatures demonstrate broad applicability across various breast cancer subtypes, grades, and treatment phases—unlike existing prognostic markers that tend to be subtype-specific. Additionally, the THR signatures have revealed four unique patient clusters with divergent survival outcomes, three of which are ER-positive and one is ER-negative. Additionally, we developed an immune-based signature (i20) which can further categorize patients within the ER-negative cluster into two subgroups: one with a highly favorable prognosis, comparable to the ER-positive subtypes, and another that is characterized by exceedingly poor survival outcomes. Conclusion: The THR-based cellular ancestry signatures open a new avenue in understanding the intricacies of breast cancer biology. These signatures offer a robust and stable framework for developing other prognostic markers, thus providing an enhanced stratification of existing breast cancer categories as well as creating new classifications with more distinct survival estimates. Citation Format: Mohamed Omar, J. Chuck Harrell, Rulla M. Tamimi, Luigi Marchionni, Tan Ince. Triple hormone receptor signatures as novel prognostic markers in breast cancer [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 3486.

Abstract 6765: A patient-derived hepatocellular carcinoma multicellular spheroid platform harnessing autologous patient serum to model the tumor microenvironment and treatment response

Abstract Hepatocellular carcinoma (HCC) is the third leading and fastest rising cause of cancer-related death worldwide. The discovery and preclinical development of compounds targeting HCC is hampered by the absence of authentic tractable systems recapitulating the heterogeneity of patient HCC tumors and the tumor microenvironment (TME). Here, we established a novel, simple and robust patient-derived multicellular tumorspheroid model based on clinical HCC tumor tissues combined with the autologous patient serum. The model was robust and reproducible independently from cancer etiology and grade recapitulating key functional features of original tissue. We show that the use of autologous patient serum was essential for the TME function. The tumorspheroids comprised the main cell compartments including epithelial cancer cells as well as all major cell populations of the TME (cancer-associated fibroblasts, macrophages and T cells). Cell type proportions were variable between tumorspheroids reflecting HCC heterogeneity. Moreover, we observed differential responses to FDA HCC approved drugs (including tyrosine kinase and checkpoint inhibitors as well as VEGF-targeting agents) between donors as in patients. This patient HCC serum-tumorspheroid model provides novel opportunities for drug discovery and development as well mechanism of action studies including compounds targeting the TME. In conclusion, this HCC serum-tumorspheroid platform will contribute to improve the dismal outcome of patients with HCC. Citation Format: Emilie Crouchet, Nuno Almeida, Sarah C. Durand, Marie Parnot, Marine A. Oudot, Fabio Giannone, Cloé Gadenne, Sara Cherradi, Natascha Roehlen, Antonio Saviano, Emanuele Emanuele Felli, Patrick Pessaux, Kazuaki Chayama, Hong Tuan F. Duong, Thomas F. Baumert, Catherine Schuster. A patient-derived hepatocellular carcinoma multicellular spheroid platform harnessing autologous patient serum to model the tumor microenvironment and treatment response [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 6765.

Abstract 6882: Exploring aryl hydrocarbon receptor expression and distribution in the tumor microenvironment, with a focus on immune cells, in various solid cancer types

Abstract Background: The aryl hydrocarbon receptor (AhR) is a transcription factor that performs various biological functions upon ligand activation. While there have been extensive studies and development of AhR-targeting anticancer therapies, including active clinical trials, research on the expression of AhR, a key biomarker, in the TME remain limited. Herein, we evaluated the nuclear and cytosolic AhR expression across five solid cancer types: head and neck squamous cell carcinoma (HNSCC), bladder cancer, colorectal cancer, esophageal cancer, and non-small cell lung cancer (NSCLC). Methods: Multiplexed immunohistochemistry (mIHC) and image cytometry were employed to investigate the AhR expression and distribution in 513 patient samples, of which 292 were from patients with one of five solid cancer types. For quantification of AhR expression levels, FCS files were generated, and the resulting data was analyzed using the R program. Results: AhR expression was predominantly high in cancer cells, followed by T cells and macrophages across all cancer types. All 513 patient samples were categorized into three clusters based on distinct expression and localization patterns of AhR. Cluster 1 exhibited high AhR expression in the cancer cell nucleus, whereas cluster 3 showed the lowest. AhR expression in the stromal macrophage nucleus was the most pronounced in cluster 2. In cluster 1, NSCLC was entirely absent, with esophageal cancer representing the largest proportion. In contrast, cluster 2 was notably different from cluster 1, with a 5% prevalence of NSCLC and a relatively higher proportion of HNSCC and colorectal cancer. The AhR expression in macrophages within cluster 2 was noticeably higher than in cluster 1. Cluster 3, which had the highest NSCLC proportion, generally displayed low AhR expression compared to other clusters, but also exhibited AhR expression in regulatory T cells (Tregs), a pattern which was not observed in other cancer types. Notably, a positive correlation was observed between the nuclear and cytosolic expressions of AhR, suggesting that AhR expression as a biomarker is independent of its subcellular localization. Furthermore, there was no clear correlation between AhR expression levels and cancer stage, grade, and metastatic status. Conclusion: We elucidated the expression profile of AhR within the TME across five cancer types, then classified the patient samples into three clusters based on their distinct AhR expression patterns. By demonstrating different expression patterns of AhR in cancer cells and immune cells, our findings are anticipated to provide a fundamental basis for clinical and immunological research on AhR-targeting therapies. Citation Format: Dong Kwon Kim, Chai Young Lee, Yu Jin Han, So Young Park, Heekyung Han, Kwangmin Na, Mi Hyun Kim, Seung Min Yang, Sujeong Baek, Youngtaek Kim, Joon Yeon Hwang, Seul Lee, Seong-san Kang, Taedong Han, Hyounmie Doh, Jongho Cho, Dajeong Kim, Daewon Cha, Min Hee Hong, Sun Min Lim, Jii Bum Lee, Jae Hwan Kim, Kyoung-Ho Pyo, Byoung Chul Cho. Exploring aryl hydrocarbon receptor expression and distribution in the tumor microenvironment, with a focus on immune cells, in various solid cancer types [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 6882.

Abstract 6231: Multi-omic characterisation of clear cell renal carcinoma reveals endogenous and exogenous processes related to its aetiology

Abstract Endogenous and exogenous exposures are associated with distinctive molecular marks in somatic tissues, including human tumours. Integrative multi-omic approaches could help improving the resolution to detect the molecular marks associated with disease aetiology. Here, we leveraged the overlapping molecular information of clear cell renal cell carcinomas (ccRCC) tumours from Mutographs cohort by integrating DNA methylation, transcriptome, and whole-genome sequencing-based somatic mutation data to explore and gain new insights into exposures linked to ccRCC aetiology. Our novel framework identified important sources of biological variance across ccRCC tumours that were summarized as molecular components. We inferred these molecular ccRCC components into independent datasets to further explore their relationship with molecular and epidemiological features of ccRCC, normal adjacent kidney tissues, and other cancer types. Our results revealed a major ccRCC molecular component correlating with cellular mitotic age-related features, particularly the mitotic epigenetic clock (age-adjusted epiTOC2), clock-like DNA mutational signatures (SBS1/ID1), and telomere attrition. This molecular component was overrepresented in ccRCC tumours in comparison with normal paired kidney tissues, associating with PBRM1 and SETD2 somatic cancer driver mutations, genome stability, tumor stage, grade and ccRCC patient survival, independently to chronological age. Pan-cancer analysis supported the mitotic-age effect, represented by this molecular component, in other cancer types. Another ccRCC component was associated with tobacco usage, the presence of tobacco related DNA mutational and methylation signatures, increased total mutation burden, and sex. This component was also related to the epigenetic regulation of xenobiotic metabolism-related genes (e.g., GSTP1), further suggesting a relationship with genotoxic compounds. We further identified molecular components related to the ccRCC tumour microenvironment and cell proliferation, including one component related to BAP1 cancer driver mutations, pro-inflammatory immune cells, and ccRCC patient survival. In conclusion, our study reports a novel framework to molecular characterize ccRCC tumours using an integrative multi-omic approach, providing additional insights into the molecular footprints of endogenous and exogenous exposures in ccRCC tumours, including molecular components with prognostic value for patients. Citation Format: Ricardo Cortez Cardoso Penha, Alexandra Sexton Oates, Sergey Senkin, Han La Park, Nicolas Alcala, Matthieu Foll, Karl Smith-Byrne, Paul Brennan, James Mckay. Multi-omic characterisation of clear cell renal carcinoma reveals endogenous and exogenous processes related to its aetiology [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 6231.

Abstract 1264: A targeted methylation-based multi-cancer early detection blood test preferentially detects high-grade prostate cancer and minimizes overdiagnosis

Abstract Background: Indolent prostate cancer (PCa) is prevalent in men in the intended use population (adults aged 50-79 years) for blood-based multi-cancer early detection (MCED) tests. It is thus important to understand detectability of PCa by MCED tests across the spectrum of disease aggressiveness, including detection of indolent disease. Methods: PCa detectability by a targeted methylation-based MCED test that interrogates circulating cfDNA was assessed in 2 studies: 1) the Circulating Cell-free Genome Atlas (CCGA, NCT02889978), a case-control study (Substudy 3) that demonstrated detection of a shared cancer signal across > 50 cancer types and accurate prediction of cancer signal origin (CSO) with an observed false positive rate of 0.5%; and 2) PATHFINDER (PF, NCT04241796), a return of results cohort study in the intended use population without clinical suspicion of cancer. MCED test performance was assessed by Gleason grade group (GG) and clinical stage, and the association between tumor methylated fraction (TMeF) and cancer detectability was investigated. Results: The CCGA3 study included 420 recently diagnosed PCa cases with a median age of 65 years (IQR, 59-70). Test sensitivity for PCa was 11.2% (47/420), and CSO accuracy was 91.5% (43/47). Median PSA was higher in detected cases (14 ng/dL, IQR 7-38 vs 6 ng/dL, IQR 5-9, p < 0.05). The MCED test detected no low grade (GG1, 0/58), 1.9% (3/157) of favorable intermediate grade (GG2), 5.1% (4/78) of unfavorable intermediate grade (GG3), and 31.9% (36/113) of high grade (GG4 & GG5) cancers. Clinical staging showed detection of 3.2% (3/95) of Stage I, 4.7% (11/235) of Stage II, 14.9% (7/47) of Stage III, and 81.5% (22/27) of Stage IV cases. Non-detected cases had better overall survival (HR 0.263, CI 0.104 - 0.533, p<0.05), and detected cases had similar survival (HR 0.672, CI 0.323 - 1.21, p=0.2) compared to SEER estimates matched for age, stage and GG. Median TMeF was higher for detected cases (0.002106, IQR 0.000350 - 0.024376 vs 0.000024, IQR 0.00001 - 0.000038, p < 0.05). Performance was similar in PF (median age 66 years, IQR 58 -70) with overall sensitivity for PCa of 5.9% (1/17), including no (0/12) detected GG1-2 cases and one of five GG3-5 cases. No Stage I or II (0/15) and 1/2 (50%) Stage III & IV cases were detected. Conclusion: In two independent prospective studies, this MCED test preferentially detected high grade, clinically significant PCa. A cancer signal detected test result with a prostate CSO prediction generally indicates the presence of aggressive PCa and should lead to a prompt diagnostic work-up. Use of this test in population-based screening programs is unlikely to exacerbate overdiagnosis of indolent PCa. Citation Format: Brandan Mahal, Matthew Margolis, Earl Hubbell, Cheng Chen, Jeffrey M. Venstrom, John Abran, Jordan J. Karlitz, Alexander W. Wyatt, Eric A. Klein. A targeted methylation-based multi-cancer early detection blood test preferentially detects high-grade prostate cancer and minimizes overdiagnosis [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 1264.

Abstract 4970: Multi-modal machine learning approaches for predicting cancer type and Gleason grade leveraging public TCGA data

Abstract Introduction: To better understand the complex and challenging nature of diseases such as cancer and for improved diagnosis, it may require the combination of multiple data modalities, such as histopathological images and omics data such as RNA-seq. By integrating these heterogeneous but complementary data, a multimodal approach unites both worlds and could achieve better synergistic results compared to using a single modality. The growing availability of large datasets such as The Cancer Genome Atlas (TCGA) with more than 10000 patients made it possible to combine different modalities to train machine learning algorithms which offers great potential to address challenging cancer related research. In this proof of concept initiative we use machine learning approaches within an open-source framework in order to leverage the potential of multimodality (Histopathology Whole Slide Images (WSI) and Genomics/RNA-seq) to build predictive AI models for cancer type and prostate Gleason score, and provide a potential to develop a quality control step. Method: We used matched WSI and RNA-Seq profiles from TCGA, including 11093 samples and 30 cancer types to develop a pancancer classification model using both modalities. For prostate Gleason score prediction 401 patients were available. Both datasets were split into a train (70%) and test (30%) components. We used a late fusion approach where we combined the RNA-seq model (linear SVM) with the WSI model (Resnet18) by multiplying the probability scores of each single-modality model. Model performance was measured with the F1 metric. Results: For cancer type prediction, the multimodality model achieved an F1 score of 0.95 on the test set. About 40% of the cancer types benefited from a synergistic effect by combining the two modalities. Cancer types and percent increase in F1 scores, respectively, that benefit most by combining modalities are: Cervical squamous cell carcinoma and endocervical adenocarcinoma (4.23%), Cholangiosarcoma (6.66%) and Uterine carcinosarcoma (4%). Interestingly, in other cancer types the combination did not result in improved predictive scores compared to a single modality model, e.g. in Rectum adenocarcinoma, Sarcoma or Stomach adenocarcinoma. For Prostate cancer grading, Gleason score prediction of patterns 3/4/5, combined multi modality model earned 0.73 F1 outperforming the single modality models. Conclusion: By combining histopathology imaging and omics modalities we demonstrated synergistic effects in predictive power for both cancer-related research questions. We show improved predictive performance in 40% of the classified cancer types by taking both modalities. Imaging or omics modalities alone can be sufficient in some cases and their strengths are very problem-specific. Citation Format: Christian Wohlfart, Eldad Klaiman, Jacub Witkowski, Michael King, Jacob Gildenblat, Ofir Etz-Hadar, Mohammad Ashtari, Antoaneta Vladimirova. Multi-modal machine learning approaches for predicting cancer type and Gleason grade leveraging public TCGA data [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 4970.

Can DNA Methylation Profiling Classify Histologic Subtypes and Grades in Soft Tissue Sarcoma?

A clear classification of the subtype and grade of soft tissue sarcoma is important for predicting prognosis and establishing treatment strategies. However, the rarity and heterogeneity of these tumors often make diagnosis difficult. In addition, it remains challenging to predict the response to chemotherapy and prognosis. Thus, we need a new method to help diagnose soft tissue sarcomas and determine treatment strategies in conjunction with traditional methods. Genetic alterations can be found in some subtypes of soft tissue sarcoma, but many other types show dysregulated gene expression attributed to epigenetic changes, such as DNA methylation status. However, research on DNA methylation profiles in soft tissue sarcoma is still insufficient to provide information to assist in diagnosis and therapeutic decisions. (1) Do DNA methylation profiles differ between normal tissue and soft tissue sarcoma? (2) Do DNA methylation profiles vary between different histologic subtypes of soft tissue sarcoma? (3) Do DNA methylation profiles differ based on tumor grade? Between January 2019 and December 2022, we treated 85 patients for soft tissue sarcomas. We considered patients whose specimens were approved for pilot research by the Human Biobank of St. Vincent's Hospital, The Catholic University of Korea, as potentially eligible. Based on this, 41% (35 patients) were eligible; 1% (one patient) was excluded because of gender mismatch between clinical and genetic data after controlling for data quality. Finally, 39 specimens (34 soft tissue sarcomas and five normal samples) were included from 34 patients who had clinical data. All tissue samples were collected intraoperatively. The five normal tissue samples were from muscle tissues. There were 20 female patients and 14 male patients, with a median age of 58 years (range 19 to 82 years). Genomic DNA was extracted from frozen tissue, and DNA methylation profiles were obtained. Genomic annotation of DNA methylation sites and hierarchical cluster analysis were performed to interpret results from DNA methylation profiling. A t-test was used to analyze different methylation probes. Benjamini-Hochberg-adjusted p value calculations were used to account for bias resulting from evaluating thousands of methylation sites. The most common histologic subtypes were liposarcoma (n = 10) and leiomyosarcoma (n = 9). The tumor grade was Fédération Nationale des Centres de Lutte Contre Le Cancer Grades 1, 2, and 3 in 3, 15, and 16 patients, respectively. DNA methylation profiling demonstrated differences between soft tissue sarcoma and normal tissue as 21,188 cytosine-phosphate-guanine sites. Despite the small number of samples, 72 of these sites showed an adjusted p value of < 0.000001, suggesting a low probability of statistical errors. Among the 72 sites, 70 exhibited a hypermethylation pattern in soft tissue sarcoma, with only two sites showing a hypomethylation pattern. Thirty of 34 soft tissue sarcomas were distinguished from normal samples using hierarchical cluster analysis. There was a different methylation pattern between leiomyosarcoma and liposarcoma at 7445 sites. Using the data, hierarchical clustering analysis showed that liposarcoma was distinguished from leiomyosarcoma. When we used the same approach and included other subtypes with three or more samples, only leiomyosarcoma and myxofibrosarcoma were separated from the other subtypes, while liposarcoma and alveolar soft-part sarcoma were mixed with the others. When comparing DNA methylation profiles between low-grade (Grade 1) and high-grade (Grades 2 and 3) soft tissue sarcomas, a difference in methylation pattern was observed at 144 cytosine-phosphate-guanine sites. Among these, 132 cytosine-phosphate-guanine sites exhibited hypermethylation in the high-grade group compared with the low-grade group. Hierarchical clustering analysis showed a division into two groups, with most high-grade sarcomas (28 of 31) separated from the low-grade group and few (3 out of 31) clustered together with the low-grade group. However, three high-grade soft tissue sarcomas were grouped with the Grade 1 cluster, and all of these sarcomas were Grade 2. When comparing Grades 1 and 2 to Grade 3, Grade 3 tumors were separated from Grades 1 and 2. We observed a different DNA methylation pattern between soft tissue sarcomas and normal tissues. Liposarcoma was distinguished from leiomyosarcoma using methylation profiling. High-grade soft tissue sarcoma samples showed a hypermethylation pattern compared with low-grade ones. Our findings indicate the need for research using methylation profiling to better understand the diverse biological characteristics of soft tissue sarcoma. Such research should include studies with sufficient samples and a variety of subtypes, as well as analyses of the expression and function of related genes. Additionally, efforts to link this research with clinical data related to treatment and prognosis are necessary. Level III, diagnostic study.

Abstract 7330: Identifying proteomic risk factors for overall, aggressive and early onset prostate cancer using mendelian randomization and tumor spatial transcriptomics

Abstract Background: Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention. Methods: We investigated the association of 2,002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomization (MR) and colocalization. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalization were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumor tissue to assess their role in tumor aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets. Results: We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which a majority were novel and replicated. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirm an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also find an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk mapped to existing therapeutic interventions. Conclusion: Our findings emphasize the importance of proteomics for improving our understanding of prostate cancer etiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumors. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer. Citation Format: Trishna A. Desai, Asa K. Hedman, Marios Dimitriou, Mine Koprulu, Sandy Figiel, Wencheng Yin, Matthias Johansson, Eleanor L. Watts, Joshua R. Atkins, Aleksandr V. Sokolov, Helgi B. Schioth, Marc J. Gunter, Konstantinos K. Tsilidis, Richard M. Martin, Maik Pietzner, Claudia Langenberg, Ian G. Mills, Alastair D. Lamb, Anders Malarstig, Tim J. Key, The PRACTICAL Consortium, Ruth C. Travis, Karl Smith-Byrne. Identifying proteomic risk factors for overall, aggressive and early onset prostate cancer using mendelian randomization and tumor spatial transcriptomics [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 7330.

Topline/Final Diagnostic Inclusion of Relevant Histologic Findings in Surgical Pathology Reporting of Carcinoma in Prostate Biopsies.

As the list of histologic parameters to include in surgical pathology reports of prostate cancer biopsies grows, some pathologists include this information in the microscopic description or summary sections of the report, whereas others include it in the "topline" or final diagnosis section. This prompted us to develop a multi-institutional survey to assess reporting trends among genitourinary (GU) pathologists. A survey instrument was shared among 110 GU pathologists via surveymonkey.com. Anonymized respondent data was analyzed. Eighty-four (76%) participants completed the survey across four continents. Most participants report tumor volume quantitation (88%), number of cores involved (89%), and both Gleason grade and Grade group (93%) in their topline; 71% include percent of pattern 4, with another 16% including it depending on cancer grade; 58% include the presence of cribriform growth pattern 4, with another 11% including it depending on cancer grade. When present, most include extraprostatic extension (90%), prostatic intraductal carcinoma (77%), and perineural invasion (77%). Inclusion of atypical intraductal proliferation (AIP) in the topline diagnosis was cancer grade-dependent, with 74% including AIP in Grade group 1, 61% in Grade group 2, 45% in Grade group 3, 30% in Grade group 4, and 26% in Grade group 5 cancers. Certain histologic features such as Gleason grade and tumor volume/cores involved are frequently included in the topline diagnosis, whereas the incorporation of other findings are more variably included. Prostate biopsy reporting remains a dynamic process with stylistic similarities and differences existing among GU pathologists.

Other-cause mortality in incidental prostate cancer.

In incidental prostate cancer (IPCa), elevated other-cause mortality (OCM) may obviate the need for active treatment. We tested OCM rates in IPCa according to treatment type and cancer grade and we hypothesized that OCM is significantly higher in not-actively-treated patients. Within the Surveillance, Epidemiology, and End Results database (2004-2015), IPCa patients were identified. Smoothed cumulative incidence plots as well as multivariable competing risks regression models were fitted to address OCM after adjustment for cancer-specific mortality (CSM). Of 5121 IPCa patients, 3655 (71%) were not-actively-treated while 1466 (29%) were actively-treated. Incidental PCa not-actively-treated patients were older and exhibited higher proportion of Gleason sum (GS) 6 and clinical T1a stage. In smoothed cumulative incidence plots, 5-year OCM was 20% for not-actively-treated versus 8% for actively-treated patients. Conversely, 5-year CSM was 5% for not-actively-treated versus 4% for actively-treated patients. No active treatment was associated with 1.4-fold higher OCM, even after adjustment for age, cancer characteristics, and CSM. According to GS, OCM reached 16%, 27%, and 35% in GS 6, 7, and 8-10 not-actively-treated IPCa patients, respectively and exceeded CSM recorded for the same three groups (2%, 6%, and 28%, respectively). Our results quantified OCM rates, confirming that in not-actively-treated IPCa patients OCM is indeed significantly higher than in their actively-treated counterparts (HR: 1.4). These observations validate the use of no active treatment in IPCa patients, in whom OCM greatly surpasses CSM (20% vs. 5%).

Diagnostic challenge and survival analysis of pulmonary oligometastases and primary lung cancer in breast cancer patients.

The aim of this study was to compare breast cancer patients with pulmonary oligometastases (POM) and primary lung cancer (PLC) and to assess whether there were differences in clinical features, CT features, and survival outcomes between the two groups. From January 2010 to December 2021, the clinical records of 437 with malignant pulmonary nodules who had breast cancer patients were reviewed. POM was identified in 45 patients and PLC in 43 patients after the initial detection of pulmonary nodules. The clinicopathological characteristics, CT appearance of pulmonary nodules, and survival of the two groups were compared. Stage II to IV breast tumors (p < 0.001), high pathological grade of breast cancer (p = 0.001), low proportion of luminal-type breast cancer (p = 0.003), and the higher serum CYFRA 21-1 level (p = 0.046) were the clinical characteristics of pulmonary nodules suggestive of POM rather than PLC. The CT features of lung nodules indicative of PLC rather than POM were the subsolid component (p < 0.001), lobulation (p = 0.010), air bronchogram (p < 0.001) and pleural indentation (p = 0.004). Ten-year survival rate for PLC was 93.2%, which was higher compared with 57.8% in those with POM (p = 0.001). Elevated serum CYFRA 21-1 levels and late-stage breast cancer may be beneficial for the diagnosis of POM. CT imaging appearances of the subsolid component, lobulation, air bronchogram, and pleural indentation increase the likelihood of PLC. Breast cancer patients with PLC presented better survival with attentive monitoring than those with POM.

A smartphone-based standalone fluorescence spectroscopy tool for cervical precancer diagnosis in clinical conditions.

Real-time prediction about the severity of noncommunicable diseases like cancers is a boon for early diagnosis and timely cure. Optical techniques due to their minimally invasive nature provide better alternatives in this context than the conventional techniques. The present study talks about a standalone, field portable smartphone-based device which can classify different grades of cervical cancer on the basis of the spectral differences captured in their intrinsic fluorescence spectra with the help of AI/ML technique. In this study, a total number of 75 patients and volunteers, from hospitals at different geographical locations of India, have been tested and classified with this device. A classification approach employing a hybrid mutual information long short-term memory model has been applied to categorize various subject groups, resulting in an average accuracy, specificity, and sensitivity of 96.56%, 96.76%, and 94.37%, respectively using 10-fold cross-validation. This exploratory study demonstrates the potential of combining smartphone-based technology with fluorescence spectroscopy and artificial intelligence as a diagnostic screening approach which could enhance the detection and screening of cervical cancer.

Digital Validation in Breast Cancer Needle Biopsies: Comparison of Histological Grade and Biomarker Expression Assessment Using Conventional Light Microscopy, Whole Slide Imaging, and Digital Image Analysis.

Given the widespread use of whole slide imaging (WSI) for primary pathological diagnosis, we evaluated its utility in assessing histological grade and biomarker expression (ER, PR, HER2, and Ki67) compared to conventional light microscopy (CLM). In addition, we explored the utility of digital image analysis (DIA) for assessing biomarker expression. Three breast pathologists assessed the Nottingham combined histological grade, its components, and biomarker expression through the immunohistochemistry of core needle biopsy samples obtained from 101 patients with breast cancer using CLM, WSI, and DIA. There was no significant difference in variance between the WSI and CLM agreement rates for the Nottingham grade and its components and biomarker expression. Nuclear pleomorphism emerged as the most variable histologic component in intra- and inter-observer agreement (kappa ≤ 0.577 and kappa ≤ 0.394, respectively). The assessment of biomarker expression using DIA achieved an enhanced kappa compared to the inter-observer agreement. Compared to each observer's assessment, DIA exhibited an improved kappa coefficient for the expression of most biomarkers with CLM and WSI. Using WSI to assess prognostic and predictive factors, including histological grade and biomarker expression in breast cancer, is acceptable. Furthermore, incorporating DIA to assess biomarker expression shows promise for substantially enhancing scoring reproducibility.

T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma.

The study on predicting the differentiation grade of colorectal cancer (CRC) based on magnetic resonance imaging (MRI) has not been reported yet. Developing a non-invasive model to predict the differentiation grade of CRC is of great value. To develop and validate machine learning-based models for predicting the differentiation grade of CRC based on T2-weighted images (T2WI). We retrospectively collected the preoperative imaging and clinical data of 315 patients with CRC who underwent surgery from March 2018 to July 2023. Patients were randomly assigned to a training cohort (n = 220) or a validation cohort (n = 95) at a 7:3 ratio. Lesions were delineated layer by layer on high-resolution T2WI. Least absolute shrinkage and selection operator regression was applied to screen for radiomic features. Radiomics and clinical models were constructed using the multilayer perceptron (MLP) algorithm. These radiomic features and clinically relevant variables (selected based on a significance level of P < 0.05 in the training set) were used to construct radiomics-clinical models. The performance of the three models (clinical, radiomic, and radiomic-clinical model) were evaluated using the area under the curve (AUC), calibration curve and decision curve analysis (DCA). After feature selection, eight radiomic features were retained from the initial 1781 features to construct the radiomic model. Eight different classifiers, including logistic regression, support vector machine, k-nearest neighbours, random forest, extreme trees, extreme gradient boosting, light gradient boosting machine, and MLP, were used to construct the model, with MLP demonstrating the best diagnostic performance. The AUC of the radiomic-clinical model was 0.862 (95%CI: 0.796-0.927) in the training cohort and 0.761 (95%CI: 0.635-0.887) in the validation cohort. The AUC for the radiomic model was 0.796 (95%CI: 0.723-0.869) in the training cohort and 0.735 (95%CI: 0.604-0.866) in the validation cohort. The clinical model achieved an AUC of 0.751 (95%CI: 0.661-0.842) in the training cohort and 0.676 (95%CI: 0.525-0.827) in the validation cohort. All three models demonstrated good accuracy. In the training cohort, the AUC of the radiomic-clinical model was significantly greater than that of the clinical model (P = 0.005) and the radiomic model (P = 0.016). DCA confirmed the clinical practicality of incorporating radiomic features into the diagnostic process. In this study, we successfully developed and validated a T2WI-based machine learning model as an auxiliary tool for the preoperative differentiation between well/moderately and poorly differentiated CRC. This novel approach may assist clinicians in personalizing treatment strategies for patients and improving treatment efficacy.

3D MR elastography-based stiffness as a marker for predicting tumor grade and subtype in cervical cancer

BackgroundIncreasing evidence has indicated that high tissue stiffness (TS) may be a potential biomarker for evaluation of tumor aggressiveness. PurposeTo investigate the value of magnetic resonance elastography (MRE)-based quantitative parameters preoperatively predicting the tumor grade and subtype of cervical cancer (CC). Study typeRetrospective. PopulationTwenty-five histopathology-proven CC patients and 7 healthy participants. Field strength/sequence3.0T, magnetic resonance imaging (MRI) (LAVA-flex) and MRE with a three-dimensional spin-echo echo-planar imaging. AssessmentThe regions of interest (ROIs) were manually drawn by two observers in tumors to measure mean TS, storage modulus (G′), loss modulus (G″) and damping ratio (DR) values. Surgical specimens were evaluated for tumor grades and subtypes. Statistical testsIntraclass correlation coefficient (ICC) was expressed in terms of inter-observer agreements. t-test or Mann-Whitney nonparametric test was used to compare the complex modulus and apparent diffusion coefficient (ADC) values between different tumor groups. Area under the receiver operating characteristic curve (AUC) analysis was used to evaluate the diagnostic performance. ResultsThe TS of endocervical adenocarcinoma (ECA) group was significantly higher than that in squamous cell carcinoma (SCC) group (5.27 kPa vs. 3.44 kPa, P = 0.042). The TS also showed significant difference between poorly and well/moderately differentiated CC (5.21 kPa vs. 3.47 kPa, P = 0.038), CC patients and healthy participants (4.18 kPa vs. 1.99 kPa, P < 0.001). The cutoff value of TS to discriminate ECA from SCC was 4.10 kPa (AUC: 0.80), while it was 4.42 kPa to discriminate poorly from well/moderately differentiated CC (AUC: 0.83), and 2.25 kPa to distinguish normal cervix from CC (AUC: 0.88), respectively. There were no significant difference in G″, DR and ADC values between any subgroups except for comparison of healthy participants and CC patients (P = 0.001, P = 0.004, P < 0.001, respectively). Data conclusion3D MRE-assessed TS shows promise as a potential biomarker to preoperatively assess tumor grade and subtype of CC.

Exploration of the relationship between tumor-infiltrating lymphocyte score and histological grade in breast cancer

BackgroundThe histological grade is an important factor in the prognosis of invasive breast cancer and is vital to accurately identify the histological grade and reclassify of Grade2 status in breast cancer patients.MethodsIn this study, data were collected from 556 invasive breast cancer patients, and then randomly divided into training cohort (n = 335) and validation cohort (n = 221). All patients were divided into actual low risk group (Grade1) and high risk group (Grade2/3) based on traditional histological grade, and tumor-infiltrating lymphocyte score (TILs-score) obtained from multiphoton images, and the TILs assessment method proposed by International Immuno-Oncology Biomarker Working Group (TILs-WG) were also used to differentiate between high risk group and low risk group of histological grade in patients with invasive breast cancer. Furthermore, TILs-score was used to reclassify Grade2 (G2) into G2 /Low risk and G2/High risk. The coefficients for each TILs in the training cohort were retrieved using ridge regression and TILs-score was created based on the coefficients of the three kinds of TILs.ResultsStatistical analysis shows that TILs-score is significantly correlated with histological grade, and is an independent predictor of histological grade (odds ratio [OR], 2.548; 95%CI, 1.648–3.941; P < 0.0001), but TILs-WG is not an independent predictive factor for grade (P > 0.05 in the univariate analysis). Moreover, the risk of G2/High risk group is higher than that of G2/Low risk group, and the survival rate of patients with G2/Low risk is similar to that of Grade1, while the survival rate of patients with G2/High risk is even worse than that of patients with G3.ConclusionOur results suggest that TILs-score can be used to predict the histological grade of breast cancer and potentially to guide the therapeutic management of breast cancer patients.

SMMF: a self-attention-based multi-parametric MRI feature fusion framework for the diagnosis of bladder cancer grading.

Multi-parametric magnetic resonance imaging (MP-MRI) may provide comprehensive information for graded diagnosis of bladder cancer (BCa). Nevertheless, existing methods ignore the complex correlation between these MRI sequences, failing to provide adequate information. Therefore, the main objective of this study is to enhance feature fusion and extract comprehensive features from MP-MRI using deep learning methods to achieve an accurate diagnosis of BCa grading. In this study, a self-attention-based MP-MRI feature fusion framework (SMMF) is proposed to enhance the performance of the model by extracting and fusing features of both T2-weighted imaging (T2WI) and dynamic contrast-enhanced imaging (DCE) sequences. A new multiscale attention (MA) model is designed to embed into the neural network (CNN) end to further extract rich features from T2WI and DCE. Finally, a self-attention feature fusion strategy (SAFF) was used to effectively capture and fuse the common and complementary features of patients' MP-MRIs. In a clinically collected sample of 138 BCa patients, the SMMF network demonstrated superior performance compared to the existing deep learning-based bladder cancer grading model, with accuracy, F1 value, and AUC values of 0.9488, 0.9426, and 0.9459, respectively. Our proposed SMMF framework combined with MP-MRI information can accurately predict the pathological grading of BCa and can better assist physicians in diagnosing BCa.

Artificial intelligence unravels interpretable malignancy grades of prostate cancer on histology images

Malignancy grading of prostate cancer (PCa) is fundamental for risk stratification, patient counseling, and treatment decision-making. Deep learning has shown potential to improve the expert consensus for tumor grading, which relies on the Gleason score/grade grouping. However, the core problem of interobserver variability for the Gleason grading system remains unresolved. We developed a novel grading system for PCa and utilized artificial intelligence (AI) and multi-institutional international datasets from 2647 PCa patients treated with radical prostatectomy with a long follow-up of ≥10 years for biochemical recurrence and cancer-specific death. Through survival analyses, we evaluated the novel grading system and showed that AI could develop a tumor grading system with four risk groups independent from and superior to the current five grade groups. Moreover, AI could develop a scoring system that reflects the risk of castration resistant PCa in men who have experienced biochemical recurrence. Thus, AI has the potential to develop an effective grading system for PCa interpretable by human experts.

Mitosis detection, fast and slow: Robust and efficient detection of mitotic figures

Counting of mitotic figures is a fundamental step in grading and prognostication of several cancers. However, manual mitosis counting is tedious and time-consuming. In addition, variation in the appearance of mitotic figures causes a high degree of discordance among pathologists. With advances in deep learning models, several automatic mitosis detection algorithms have been proposed but they are sensitive to domain shift often seen in histology images. We propose a robust and efficient two-stage mitosis detection framework, which comprises mitosis candidate segmentation (Detecting Fast) and candidate refinement (Detecting Slow) stages. The proposed candidate segmentation model, termed EUNet, is fast and accurate due to its architectural design. EUNet can precisely segment candidates at a lower resolution to considerably speed up candidate detection. Candidates are then refined using a deeper classifier network, EfficientNet-B7, in the second stage. We make sure both stages are robust against domain shift by incorporating domain generalization methods. We demonstrate state-of-the-art performance and generalizability of the proposed model on the three largest publicly available mitosis datasets, winning the two mitosis domain generalization challenge contests (MIDOG21 and MIDOG22). Finally, we showcase the utility of the proposed algorithm by processing the TCGA breast cancer cohort (1,124 whole-slide images) to generate and release a repository of more than 620K potential mitotic figures (not exhaustively validated).