Tumor Phenotype Research Articles

Artificial intelligence in immunotherapy PET/SPECT imaging.

Immunotherapy has dramatically altered the therapeutic landscape for oncology, but more research is needed to identify patients who are likely to achieve durable clinical benefit and those who may develop unacceptable side effects. We investigated the role of artificial intelligence in PET/SPECT-guided approaches for immunotherapy-treated patients. We performed a scoping review of MEDLINE, CENTRAL, and Embase databases using key terms related to immunotherapy, PET/SPECT imaging, and AI/radiomics through October 12, 2022. Of the 217 studies identified in our literature search, 24 relevant articles were selected. The median (interquartile range) sample size of included patient cohorts was 63 (157). Primary tumors of interest were lung (n = 14/24, 58.3%), lymphoma (n = 4/24, 16.7%), or melanoma (n = 4/24, 16.7%). A total of 28 treatment regimens were employed, including anti-PD-(L)1 (n = 13/28, 46.4%) and anti-CTLA-4 (n = 4/28, 14.3%) monoclonal antibodies. Predictive models were built from imaging features using univariate radiomics (n = 7/24, 29.2%), radiomics (n = 12/24, 50.0%), or deep learning (n = 5/24, 20.8%) and were most often used to prognosticate (n = 6/24, 25.0%) or describe tumor phenotype (n = 5/24, 20.8%). Eighteen studies (75.0%) performed AI model validation. Preliminary results suggest broad potential for the application of AI-guided immunotherapy management after further validation of models on large, prospective, multicenter cohorts. This scoping review describes how artificial intelligence models are built to make predictions based on medical imaging and explores their application specifically in the PET and SPECT examination of immunotherapy-treated cancers. • Immunotherapy has drastically altered the cancer treatment landscape but is known to precipitate response patterns that are not accurately accounted for by traditional imaging methods. • There is an unmet need for better tools to not only facilitate in-treatment evaluation but also to predict, a priori, which patients are likely to achieve a good response with a certain treatment as well as those who are likely to develop side effects. • Artificial intelligence applied to PET/SPECT imaging of immunotherapy-treated patients is mainly used to make predictions about prognosis or tumor phenotype and is built from baseline, pre-treatment images. Further testing is required before a true transition to clinical application can be realized.

Emerging need of molecular profiling in hepatobiliary cancer

: Gallbladder cancer is a rare malignancy but represents almost 50% of all biliary tract cancer. Biliary cancers are highly fatal malignancies with a 5-year survival rate of approximately 20%. The prognosis of gallbladder cancer is poor due to the aggressive tumor biology, late presentation, complicated anatomic position, and advanced stage at diagnosis. Locally advanced and metastatic disease treatment is with palliative chemotherapy. Alarming sign of gallbladder cancer is overall decreased in incidence in older patients but increased in the younger population. So many mutations have been reported for the gall bladder cancer till date. : A prospective observational study was conducted over a period of 1 year at Asian Institute of Medical Sciences Faridabad which includes hepatobiliary carcinoma patients who are at stage III and stage IV of cancer. After getting the consent formalin fixed paraffin embedded biopsy samples, and 5 ml serum sample was collected in serum separator tube (SST). A whole genome sequencing was performed using Illumina HiSEQ, Illumina (NGS) technology, allows for high-throughput sequencing of DNA and RNA. Illumina's NGS is based on "sequencing by Synthesis" to detect the mutations.: Most common mutation found was in the P53 gene. TP53 (p.Arg175His), TP53 (p.Arg306Ter), TP53 (p.Cys238Tyr), TP53 (p.Leu43Ter), TP53 (p.Glu339Ter), TP53 (p.Pro190Leu). Mutations in the TP53 gene are a common feature of carcinoma of the gallbladder, and are associated with a more aggressive tumor phenotype, resistance to chemotherapy, and poorer overall survival.

SALL4 promotes cancer stem-like cell phenotype and radioresistance in oral squamous cell carcinomas via methyltransferase-like 3-mediated m6A modification

Radioresistance imposes a great challenge in reducing tumor recurrence and improving the clinical prognosis of individuals having oral squamous cell carcinoma (OSCC). OSCC harbors a subpopulation of CD44(+) cells that exhibit cancer stem-like cell (CSC) characteristics are involved in malignant tumor phenotype and radioresistance. Nevertheless, the underlying molecular mechanisms in CD44( + )-OSCC remain unclear. The current investigation demonstrated that methyltransferase-like 3 (METTL3) is highly expressed in CD44(+) cells and promotes CSCs phenotype. Using RNA-sequencing analysis, we further showed that Spalt-like transcription factor 4 (SALL4) is involved in the maintenance of CSCs properties. Furthermore, the overexpression of SALL4 in CD44( + )-OSCC cells caused radioresistance in vitro and in vivo. In contrast, silencing SALL4 sensitized OSCC cells to radiation therapy (RT). Mechanistically, we illustrated that SALL4 is a direct downstream transcriptional regulation target of METTL3, the transcription activation of SALL4 promotes the nuclear transport of β-catenin and the expression of downstream target genes after radiation therapy, there by activates the Wnt/β-catenin pathway, effectively enhancing the CSCs phenotype and causing radioresistance. Herein, this study indicates that the METTL3/SALL4 axis promotes the CSCs phenotype and resistance to radiation in OSCC via the Wnt/β-catenin signaling pathway, and provides a potential therapeutic target to eliminate radioresistant OSCC.

E-CADERIN, N-CADERIN, SLUG, SNAIL, and TWIST contribute to epithelial-mesenchymal transition in salivary gland tumors.

Transcription factors are important in the epithelial-mesenchymal transition process and are possibly related to the development of a more invasive tumor phenotype. Thus, the objective of this study was to analyze the expression and identify the localization of cellular markers related to the epithelial-mesenchymal transition process in salivary gland tumors. The expression and localization of E-CADERIN, N-CADERIN, SLUG, SNAIL, and TWIST were evaluated, using immunohistochemistry, in 48 salivary gland tumors, being 17 pleomorphic adenomas (PA), 14 adenoid cystic carcinomas (ACC), and 17 mucoepidermoid carcinomas (MEC). these proteins were compared to clinical and histopathologic parameters. normal gland tissues were included for immunohistochemical comparisons. ACC and MEC cases showed higher expression of SNAIL compared to PA. MEC showed high expression of SLUG and TWIST. Low expression of N-CADHERIN, SNAIL, and TWIST in ACC was frequent in T3 and T4. High expression of TWIST in MEC was more frequent at age ≥ 40 years A positive correlation was only observed between N-cadherin/SNAIL in ACC, between SNAIL/TWIST in MEC, and between SLUG/TWIST in PA. This study provided insight into EMT-related proteins (E-cadherin, N-cadherin, SNAIL, SLUG, and TWIST) and their contribution to the maintenance of morphogenesis and the development of the salivary gland tumors and showed a positive correlation among N-CADHERIN/SNAIL in ACC and SNAIL/TWIST in MEC.

Viscoelastic stiffening of gelatin hydrogels for dynamic culture of pancreatic cancer spheroids

The tumor microenvironment (TME) in pancreatic adenocarcinoma (PDAC) is a complex milieu of cellular and non-cellular components. Pancreatic cancer cells (PCC) and cancer-associated fibroblasts (CAF) are two major cell types in PDAC TME, whereas the non-cellular components are enriched with extracellular matrices (ECM) that contribute to high stiffness and fast stress-relaxation. Previous studies have suggested that higher matrix rigidity promoted aggressive phenotypes of tumors, including PDAC. However, the effects of dynamic viscoelastic matrix properties on cancer cell fate remain largely unexplored. The focus of this work was to understand the effects of such dynamic matrix properties on PDAC cell behaviors, particularly in the context of PCC/CAF co-culture. To this end, we engineered gelatin-norbornene (GelNB) based hydrogels with a built-in mechanism for simultaneously increasing matrix elastic modulus and viscoelasticity. Two GelNB-based macromers, namely GelNB-hydroxyphenylacetic acid (GelNB-HPA) and GelNB-boronic acid (GelNB-BA), were modularly mixed and crosslinked with 4-arm poly(ethylene glycol)-thiol (PEG4SH) to form elastic hydrogels. Treating the hybrid hydrogels with tyrosinase not only increased the elastic moduli of the gels (due to HPA dimerization) but also concurrently produced 1,2-diols that formed reversible boronic acid-diol bonding with the BA groups on GelNB-BA. We employed patient-derived CAF and a PCC cell line COLO-357 to demonstrate the effect of increasing matrix stiffness and viscoelasticity on CAF and PCC cell fate. Our results indicated that in the stiffened environment, PCC underwent epithelial-mesenchymal transition. In the co-culture PCC and CAF spheroid, CAF enhanced PCC spreading and stimulated collagen 1 production. Through mRNA-sequencing, we further showed that stiffened matrices, regardless of the degree of stress-relaxation, heightened the malignant phenotype of PDAC cells. Statement of significanceThe pancreatic cancer microenvironment is a complex milieu composed of various cell types and extracellular matrices. It has been suggested that stiffer matrices could promote aggressive behavior in pancreatic cancer, but the effect of dynamic stiffening and matrix stress-relaxation on cancer cell fate remains largely undefined. This study aimed to explore the impact of dynamic changes in matrix viscoelasticity on pancreatic ductal adenocarcinoma (PDAC) cell behavior by developing a hydrogel system capable of simultaneously increasing stiffness and stress-relaxation on demand. This is achieved by crosslinking two gelatin-based macromers through orthogonal thiol-norbornene photochemistry and post-gelation stiffening with mushroom tyrosinase. The results revealed that higher matrix stiffness, regardless of the degree of stress relaxation, exacerbated the malignant characteristics of PDAC cells.

Hypoxic transcriptomes predict survival and tumor-infiltrating immune cell composition in cutaneous melanoma.

Hypoxia has established associations with aggressive tumor phenotypes in many cancers. However, it is not currently understood whether tumor hypoxia levels map to distinct immune infiltrates in cutaneous melanoma, potentially unveiling novel therapeutic targets. To this end, we leveraged a previously identified seven-gene hypoxia signature to grade hypoxia levels of 460 cutaneous melanomas obtained from the Broad Institute GDAC Firehose portal. CIBERSORTx ( https://cibersortx.stanford.edu/ ) was employed to calculate the relative abundance of 22 mature human hematopoietic populations. Clinical outcomes and immune cell associations were assessed by computational means. Results indicated that patients with high-hypoxia tumors reported significantly worse overall survival and correlated with greater Breslow depth, validating the in-silico methodology. High-hypoxia tumors demonstrated increased infiltration of activated and resting dendritic cells, resting mast cells, neutrophils, and resting NK cells, but lower infiltration of gamma-delta T cells. These data suggest that high tumor hypoxia correlates with lower survival probability and distinct population differences of several tumor-infiltrating leukocytes in cutaneous melanomas.

Integrating Artificial Intelligence and PET Imaging for Drug Discovery: A Paradigm Shift in Immunotherapy.

The integration of artificial intelligence (AI) and positron emission tomography (PET) imaging has the potential to become a powerful tool in drug discovery. This review aims to provide an overview of the current state of research and highlight the potential for this alliance to advance pharmaceutical innovation by accelerating the development and deployment of novel therapeutics. We previously performed a scoping review of three databases (Embase, MEDLINE, and CENTRAL), identifying 87 studies published between 2018 and 2022 relevant to medical imaging (e.g., CT, PET, MRI), immunotherapy, artificial intelligence, and radiomics. Herein, we reexamine the previously identified studies, performing a subgroup analysis on articles specifically utilizing AI and PET imaging for drug discovery purposes in immunotherapy-treated oncology patients. Of the 87 original studies identified, 15 met our updated search criteria. In these studies, radiomics features were primarily extracted from PET/CT images in combination (n = 9, 60.0%) rather than PET imaging alone (n = 6, 40.0%), and patient cohorts were mostly recruited retrospectively and from single institutions (n = 10, 66.7%). AI models were used primarily for prognostication (n = 6, 40.0%) or for assisting in tumor phenotyping (n = 4, 26.7%). About half of the studies stress-tested their models using validation sets (n = 4, 26.7%) or both validation sets and test sets (n = 4, 26.7%), while the remaining six studies (40.0%) either performed no validation at all or used less stringent methods such as cross-validation on the training set. Overall, the integration of AI and PET imaging represents a paradigm shift in drug discovery, offering new avenues for more efficient development of therapeutics. By leveraging AI algorithms and PET imaging analysis, researchers could gain deeper insights into disease mechanisms, identify new drug targets, or optimize treatment regimens. However, further research is needed to validate these findings and address challenges such as data standardization and algorithm robustness.

Pathological response and tumor stroma immunogenic features predict long-term survival in non-small cell lung cancer after neoadjuvant chemotherapy.

Major pathological response (MPR) has become a surrogate endpoint for overall survival (OS) in non-small cell lung cancer (NSCLC) after neoadjuvant therapy, however, the prognostic histologic features and optimal N descriptor after neoadjuvant therapy are poorly defined. We retrospectively analyzed data from 368 NSCLC patients who underwent surgery after neoadjuvant chemotherapy (NAC) from January 2010 to December 2020. The percentage of residual viable tumors in the primary tumor, lymph nodes (LN), and inflammation components within the tumor stroma were comprehensively reviewed. The primary endpoint was OS. Of the 368 enrolled patients, 12.0% (44/368) achieved MPR in the primary tumor, which was associated with significantly better OS (HR, 0.36 0.17-0.77, p = 0.008) and DFS (HR = 0.59, 0.36-0.92, p = 0.038). In patients who did not have an MPR, we identified an immune-activated phenotype in primary tumors, characterized by intense tumor-infiltrating lymphocyte or multinucleated giant cell infiltration, that was associated with similar OS and DFS as patients who had MPR. Neoadjuvant pathologic grade (NPG), consisting of MPR and immune-activated phenotype, identified 30.7% (113/368) patients that derived significant OS (HR 0.28, 0.17-0.46, p < 0.001) and DFS (HR 0.44, 0.31-0.61, p < 0.001) benefit from NAC. Moreover, the combination of NPG and the number of positive LN stations (nS) in the multivariate analysis had a higher C-index (0.711 vs. 0.663, p < 0.001) than the ypTNM Stage when examining OS. NPG integrated with nS can provide a simple, practical, and robust approach that may allow for better stratification of patients when evaluating neoadjuvant chemotherapy in clinical practice.

Biopsy Techniques for Musculoskeletal Tumors: Basic Principles and Specialized Techniques.

Biopsy is a pivotal component in the diagnostic process of bone and soft tissue tumors. The objective is to obtain adequate tissue without compromising local tumor dissemination and the patient's survival. This review explores contemporary principles and practices in musculoskeletal biopsies, emphasizing the critical role of diagnostic accuracy while also delving into the evolving landscape of liquid biopsies as a promising alternative in the field. A thorough literature search was done in PubMed and Google Scholar as well as in physical books in libraries to summarize the available biopsy techniques for musculoskeletal tumors, discuss the available methods, risk factors, and complications, and to emphasize the challenges related to biopsies in oncology. Research articles that studied the basic principles and specialized techniques of biopsy techniques in tumor patients were deemed eligible. Their advantages and disadvantages, technical and pathophysiological mechanisms, and possible risks and complications were reviewed, summarized, and discussed. An inadequately executed biopsy may hinder diagnosis and subsequently impact treatment outcomes. All lesions should be approached with a presumption of malignancy until proven otherwise. Liquid biopsies have emerged as a potent non-invasive tool for analyzing tumor phenotype, progression, and drug resistance and guiding treatment decisions in bone sarcomas and metastases. Despite advancements, several barriers remain in biopsies, including challenges related to costs, scalability, reproducibility, and isolation methods. It is paramount that orthopedic oncologists work together with radiologists and pathologists to enhance diagnosis, patient outcomes, and healthcare costs.

Extra-Capsular Spread of Lymph Node Metastasis in Oral, Oropharyngeal and Hypopharyngeal Cancer: A Comparative Subsite Analysis

Background: The extra-capsular spread (ECS) of lymph node metastasis (LNM) is a hallmark of aggressive primary tumor phenotype in head and neck cancer (HNC); however, the factors influencing ECS are poorly understood. Patients and Methods: This was a retrospective study, including 190 cases of oral tongue cancer (OTC), 148 cases of oropharyngeal cancer (OPC) (118 HPV-positive and 30 HPV-negative), and 100 cases of hypopharyngeal cancer (HPC). Tumor dimension, tumor biological variables (lymphovascular/perineural invasion and histologic grade), and LNM variables (LNM number and size) were analyzed according to the presence of ECS using multivariable logistic regression and receiver operating characteristic (ROC) curve analyses. Results: LNM variables were significant factors for ECS in all subsites of HNC (p &lt; 0.05), except HPV-positive OPC. In OTC, tumor dimensional variables were significantly related to ECS (p &lt; 0.01). Meanwhile, in OPC and HPC, neither the primary tumor dimension nor the T status were significant factors for ECS occurrence. The predictability of ECS by ROC curve using multiple variables was 0.819 [95% confidence interval: 0.759–0.878] in OTC, 0.687 [0.559–0.815] in HPV-positive OPC, 0.823 [0.642–1.000] in HPV-negative OPC, and 0.907 [0.841–0.973] in HPC. Conclusion: LNM variables were correlated with ECS occurrence for most HNC subsites, and site-dependent primary tumor characteristics might contribute differentially to the ECS development of LNM in HNC.

How Imaging Advances Are Defining the Future of Precision Radiation Therapy.

Radiation therapy is fundamental in the treatment of cancer. Imaging has always played a central role in radiation oncology. Integrating imaging technology into irradiation devices has increased the precision and accuracy of dose delivery and decreased the toxic effects of the treatment. Although CT has become the standard imaging modality in radiation therapy, the development of recently introduced next-generation imaging techniques has improved diagnostic and therapeutic decision making in radiation oncology. Functional and molecular imaging techniques, as well as other advanced imaging modalities such as SPECT, yield information about the anatomic and biologic characteristics of tumors for the radiation therapy workflow. In clinical practice, they can be useful for characterizing tumor phenotypes, delineating volumes, planning treatment, determining patients' prognoses, predicting toxic effects, assessing responses to therapy, and detecting tumor relapse. Next-generation imaging can enable personalization of radiation therapy based on a greater understanding of tumor biologic factors. It can be used to map tumor characteristics, such as metabolic pathways, vascularity, cellular proliferation, and hypoxia, that are known to define tumor phenotype. It can also be used to consider tumor heterogeneity by highlighting areas at risk for radiation resistance for focused biologic dose escalation, which can impact the radiation planning process and patient outcomes. The authors review the possible contributions of next-generation imaging to the treatment of patients undergoing radiation therapy. In addition, the possible roles of radio(geno)mics in radiation therapy, the limitations of these techniques, and hurdles in introducing them into clinical practice are discussed. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

Abstract A004: Identification of genetic susceptibility loci for ErbB2-driven mammary tumor development and metastasis using Collaborative Cross mice and human relevant validation

Abstract Introduction Epidermal growth factor receptor 2 (HER2)-targeted therapies are effective treatments for HER2+ breast cancers (BC), but intrinsic and acquired resistance hampers their clinical benefit. Therefore, new biological insights into HER2-driven cancers are needed to conquer drug resistance. Here, we used a genetically diverse and population-based Collaborative Cross (CC) mouse model to identify genetic susceptibility to tumor development and metastasis driven by HER2. Materials and methods 732 F1 hybrid mice were generated from a cross between FVB/N MMTV-ErbB2 and 30 CC strains and were monitored for mammary tumor development within two years. Tumor onset, multiplicity and metastasis were assessed. Genome wide association study (GWAS) was used to identify genetic variations controlling different tumor phenotypes. Polygenetic risk score was created for each phenotype using multivariate analysis. We systematically evaluated the clinical value regarding prognosis and therapeutic responses of mouse tumor susceptibility gene signature (mTSGS) in human breast cancer (BC) using publicly available cohorts including I-SPY2 clinical trial cohorts. Results We observed huge differences in tumor onset, multiplicities, and metastasis across 30 CC strains. In addition to lung metastasis, we found liver/kidney metastasis in some CC strains. GWAS identified 1,525 SNPs significantly associated with tumor onset (p&amp;lt;1.00E-30) corresponding to 287 known genes, 800 SNPs significantly associated with number of tumors (p&amp;lt;1.00E-15) corresponding to 203 known genes, 568 SNPs significantly associated with lung metastasis (p&amp;lt;1.00E-4) corresponding to 189 known genes, and 23 SNPs significantly associated with liver metastasis (p&amp;lt;1.00E-4) corresponding to 12 known genes. Multivariate analyses identified the SNPs in 8 genes (Stx6, Ramp1, Traf3ip1, Nckap5, Pfkfb2, Trmt1l, Rprd1b and Rer1) independently associated with age onset, in 11 genes (Stx6, Sepsecs, Rhobtb1, Tsen15, Abcc3, Arid5b, Tnr, Dock2, Tti1, Fam81a and Oxr1) independently associated with number of tumors, and in 2 genes (Plxna2 and Tbc1d31) independently associated with tumor metastasis, which were pooled together as mTSGS. To evaluate the impact of mTSGS on human BC, we established mTSGS score (mTSGSS) based on their transcriptional levels in BC. We found that mTSGSS is significantly and independently associated with overall, disease free and progression free survival compared to clinical factors and PAM50 molecular subtype in different cohorts. Additionally, patients with low mTSGSS have a higher pathological complete response (pCR) rate in comparison to these with high mTSGSS for 6 of 13 treatment regimens in I-SPY2 cohort. Multivariate logistic regression analysis showed that predictive value of mTSGSS in pCR is independent of MammaPrint (MP) score in these treatment regimens. Conclusion Our findings lead significantly to biological insights into ErbB2-driven cancers and indicate that mTSGSS can serve as a biomarker for tailoring treatment to BC patients. Citation Format: Hang Chang, Jamie L Inman, Antoine M Snijders, Jian-Hua Mao. Identification of genetic susceptibility loci for ErbB2-driven mammary tumor development and metastasis using Collaborative Cross mice and human relevant validation [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr A004.

Abstract B014: Bioinformatic and in-vitro analyses identify cAMP/PKA/CREB signaling as a major molecular alteration in invasive lobular breast cancer

Abstract Invasive lobular cancer (ILC) is the second most common subtype of breast cancers after invasive carcinoma of no special type (NST) but associated with worse outcomes. Despite the known molecular differences between ILC and NST, ILC treatment is the same as stage matched NST making the development of ILC targeted therapies an urgent need. Therefore, this study was conducted to identify novel molecular alterations in ILC that could be exploited for therapeutic purposes. Differential gene expression and Geneset Enrichment and Variation analyses were performed on RNA-seq data from three large public breast cancer databases – the Sweden Cancerome Analysis Network-Breast (SCAN-B), The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). Pathways enriched in overlapping differentially expressed genes from these datasets were clustered using Jaccard index to identify novel pathways related to ILC. The cAMP/PKA/CREB signaling was studied in ILC, ILC-like and NST cell lines and patient-derived organoids (POD) using Forskolin, an activator of the pathway. Clinicopathological features of patients with ILC and NST in SCAN-B were similar to prior population-based studies. There was a consistent pattern of up-regulation of cAMP/PKA/CREB related signaling in ILC compared to NST tumors in SCAN-B, TCGA and METABRIC. Treatment with forskolin resulted in a greater increase in phospho-CREB in ILC cell lines and organoids compared to NST. CRISPR deletion of CDH1 in NST cell lines did not alter response of cells to forskolin as measured by phospho-CREB. Co-immunofluorescence supported the enhanced nuclear localization and subsequent phosphorylation of CREB at Serine 133 in ILC cell lines. Forskolin treatment caused growth inhibition in ILC and NST cells, with ILC cell lines being more sensitive to forskolin-mediated growth inhibition. Collectively the data indicate that the cAMP/PKA/CREB signaling is higher in ILC than NST tumors. This was consistent across three separate cancer datasets and was validated in cell lines and organoids. Loss of CDH1 is not sufficient to mediate this phenotype in lobular tumors. Future studies should investigate the mechanisms for differential cAMP/PKA/CREB signaling in ILC and NST and investigate the potential for therapeutic targeting especially in patients with ILC. Citation Format: Abdalla Wedn, Susrutha Puthanmadhom-Narayanan, Adrian Lee, Steffi Oesterreich. Bioinformatic and in-vitro analyses identify cAMP/PKA/CREB signaling as a major molecular alteration in invasive lobular breast cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr B014.

Abstract B039: SAMPLER: Unsupervised representations of whole slide images for tumor phenotype prediction

Abstract Deep learning has revolutionized digital pathology, allowing for automatic analysis of hematoxylin and eosin (H&amp;E) stained whole slide images (WSIs) for various tasks, such as the classification of cancers into clinical subtypes. In such analyses, WSIs are broken down into smaller images, hereafter called tiles, for efficient processing, with each tile encoded by a deep learning backbone. To reconstruct a slide level representation, a widely applied approach is to combine tile-level features using attention-based deep learning models for each downstream prediction task. These training strategies are (a) computationally intensive, (b) challenging to optimize, (c) sensitive to stain variations across datasets, and (d) require sufficiently large and labeled datasets for supervised training, which are not always available. We propose SAMpling of multiscale empirical distributions for LEarning Representations (SAMPLER), a fully statistical approach to generate WSI representations by encoding the empirical cumulative distribution function (CDF) of multiscale tile features. We evaluated this approach by training logistic regression classifiers from SAMPLER representations. SAMPLER-based classifiers were able to accurately separate subtypes of breast carcinoma (BRCA: AUC=0.911 ± 0.029), subtypes of non-small cell lung carcinoma (NSCLC: AUC=0.940±0.018), and subtypes of renal cell carcinoma (RCC: AUC=0.987±0.006) diagnostic slides of the cancer genome atlas (TCGA). Performance was similar to fully deep learning attention models but &amp;gt;100 times faster. We further validated out models on external test sets. Histopathological review confirms that SAMPLER-identified high attention tiles contain tumor morphological features specific to the tumor type, while low attention tiles contain fibrous stroma, blood, or tissue folding artifacts. SAMPLER is a fast and accurate approach for analyzing WSIs, with greatly improved scalability over attention methods to benefit digital pathology analysis. Citation Format: Patience Mukashyaka, Todd B. Sheridan, Ali Foroughi pour, Jeffrey H. Chuang. SAMPLER: Unsupervised representations of whole slide images for tumor phenotype prediction [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr B039.

Abstract A014: Oncogenic PIK3CA on therapeutic resistance and cellular heterogeneity in malignant vascular cancer

Abstract Angiosarcoma is an extremely rare and aggressive cancer that originates from endothelial cells lining blood or lymphatic vessels. The PI3K-AKT pathway plays a crucial role in angiogenesis and pathogenesis of this vascular cancer. Multi-omics studies have identified oncogenic mutations in the PIK3CA, PIK3R1, and PTEN genes involved in the PI3K-AKT pathway in human and canine angiosarcomas. Recently, the FDA approved alpelisib, a PI3K-alpha specific inhibitor, for breast cancer patients with PIK3CA mutations. However, less than 30% of patients treated with alpelisib showed an overall response. Thus, understanding how PIK3CA mutant cells acquire therapeutic resistance is of high importance, yet it remains largely unexplored. In this study, we aimed to determine the molecular effects of PIK3CA mutations on therapeutic resistance against PI3K inhibitors in angiosarcomas. Specifically, PIK3CA H1047R mutations were induced in DHSA-1426 and COSB canine hemangiosarcoma cell lines using CRISPR/Cas9-mediated gene editing, and mutant clones were clonally selected. RNA-seq analysis from the PIK3CA mutant cells and 74 canine hemangiosarcoma tissue samples demonstrated that the activation of the oncogenic PI3K pathway increased cytokine and chemokine secretion, possibly contributing to an immunogenic and inflammatory tumor phenotype. Alpelisib completely abrogated AKT phosphorylation and decreased cytokine and chemokine expression in PIK3CA mutant cells. However, alpelisib showed only moderate potency in inhibiting cell proliferation of the mutant cells, demonstrating partial resistance. Subsequently, our single-cell RNA-seq analysis identified drug-resistant cell clusters within the total cell population. Marker gene analysis revealed that the resistant cell clusters upregulated genes associated with the G protein pathway (GNAI1, VAV3, and GNG11), adenylyl cyclase pathway (PDE4D and PDE7B), calcium signaling pathway (RYR3 and CAMK2D), TGF-beta pathway (TGFB2 and TGFBR2), and the anti-apoptotic gene BCL2. These pathways are all associated with RAS-MAPK signaling, and we confirmed enhanced phosphorylation of ERK upon alpelisib treatment through western blotting. Further, pseudo-time analysis using Monocle 3 indicated that the resistant cell clusters shared a similar differentiation time point with the sensitive cell clusters within the PIK3CA mutant population. Our single-cell trajectory data suggest that therapeutic resistance to alpelisib could be independent of differentiation level. Instead, it may involve an evolutionary program controlled by specific selective pressures between resistant and sensitive cells, potentially associated with immune regulatory mechanisms. Our ongoing work is evaluating whether a cellular autonomous program that regulates cytokine secretion induced by PIK3CA mutation contributes to therapeutic resistance in the tumor microenvironment through in vivo experiment. This study will provide insights into potential mechanisms governing the cellular evolution process of PIK3CA mutant cells, which promote drug resistance. Citation Format: Donghee Lee, Emma Kozurek, Md Abdullah, Rong Li, Joanne Kim, Erin B. Dickerson, Jong Hyuk Kim. Oncogenic PIK3CA on therapeutic resistance and cellular heterogeneity in malignant vascular cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr A014.

BRAF V600E Mutation Lacks Association with Poorer Clinical Prognosis in Papillary Thyroid Carcinoma.

Previous literatures showed wide range of prevalence of BRAF V600E in papillary thyroid carcinoma (PTC). The correlation of BRAF V600E mutation with aggressive tumor characteristics and poor prognosis is controversial. The present study was designed to evaluate the association between BRAF V600E mutation with clinicopathological factors and tumor recurrence. We performed a retrospective chart review of 672 patients who underwent thyroid surgery for PTC during 2013 and 2018. The prevalence of the BRAF V600E mutation was studied. Its correlation with clinicopathologic characteristics and aggressive features, including macroscopic extrathyroidal extension, lymph node metastasis, and distant metastasis, were analyzed with Fisher's exact test. A total of 672 patients who underwent surgical treatment for PTC were included in this study with a mean age of 49.7 (± 13.2) years; 76.8% of the patients were detected with BRAF V600E mutation. Mean tumor size was 1.30 (± 1.07)cm. A significant association was demonstrated between negative BRAF V600E and larger primary tumor size, distant metastasis, and advanced staging (p < 0.05), whereas there was no significant association with age, sex, lymph node metastasis, extrathyroidal extension, and multicentricity. Kaplan-Meier curve showed similar disease-free survival rate between the two groups. Negative BRAF V600E tumors show more aggressive behavior with a higher risk of developing distant metastasis in patients with PTC. The usefulness of BRAF in predicting the prognosis of PTC remains questionable. Further molecular analysis should be conducted for contribution to aggressive tumor phenotype.

Targeted Sequencing in Gastric Cancer: Association with Tumor Molecular Characteristics and FLOT Therapy Effectiveness.

Heterogeneity of gastric cancer (GC) is the main trigger of the disease's relapse. The aim of this study was to investigate the connections between targeted genes, cancer clinical features, and the effectiveness of FLOT chemotherapy. Twenty-one patients with gastric cancers (GCs) were included in this study. Tumor-targeted sequencing was conducted, and real-time PCR was used to assess the expression of molecular markers in tumors. Seven patients with stabilization had mutations that were related to their response to therapy and were relevant to the tumor phenotype. Two patients had two mutations. The number of patients with TP53 mutations increased in HER2-positive tumor status. PD-L1-positive cancers had mutations in KRAS, TP53, PIK3CA, PTEN, and ERBB, which resulted in an increase in PD-1 expression. TP53 mutation and PTEN mutation are associated with changes in factors associated with neoangiogenesis. In concusion, patients who did not have aggressive growth markers that were verified by molecular features had the best response to treatment, including complete morphologic regression.

Abstract A009: Targeting Structural Variants (DNA palindromes) in plasma cell-free DNA for Liquid Biopsy-based Cancer Detection

Abstract We aim to expand the utilities of our genome-wide detection method of chromosomal structural aberrations (structural variants, SVs) for blood-based cancer detection (liquid biopsy). SVs, such as genomic amplification, are prevalent DNA abnormalities and define tumor phenotypes. With cell-free DNA (cfDNA) in plasma, a genome-wide assay for SVs seems feasible for cancer detection. However, most current tests probe a part of the genome for cancer gene mutations and differential DNA methylation. Considering liquid biopsy's promise in clinics, approaches targeting SVs must be considered and developed. The low tumor fraction in plasma cfDNA is a major obstacle to developing liquid biopsy-based tests. Targeted assays overcome low signals by ultra-deep NGS, yet deep sequencing for the entire genome for SV detection would be costly and computationally challenging. In this regard, we have an established approach for enriching SVs (DNA palindromes or inverted repeats, also called fold-back inversions) from total genomic DNA to determine their frequency and genomic locations by sequencing (genome-wide analysis of palindrome formation, GAPF-seq). DNA palindromes arise from common events of genome instability, such as illegitimate repair of DNA double-strand breaks and telomere-telomere fusions, as an early step of genomic amplification. Therefore, a technique to identify DNA palindromes could serve as a tumor DNA detection test across tumor types, including breast cancer. The simple GAPF-seq procedure confers advantages over other methods because it simultaneously amplifies the target signal and reduces background noise. GAPF-seq exploits the inherent propensity of denatured DNA palindromes to form double-stranded DNA (dsDNA) by intra-molecular annealing. Genomic DNA will be denatured and quickly renatured to enrich DNA palindromes to favor intra-molecular annealing. Single-strand-specific nuclease S1 will eliminate the remaining single-stranded, non-palindromic DNA. The dsDNA from palindromes will be amplified by the process of preparing libraries for NGS, by which DNA palindromes appear as coverage peaks in the genome. As a proof-of-concept, we examined the read coverage of nearly 3 million 1-kb bins from low coverage NGS GAPF-seq data for 39 breast paired tumor/normal DNA. We found that the chromosomal distribution of the top 1000 high-coverage bins (GAPF profiles) could effectively differentiate breast tumor DNA, including all stage I tumor DNA, from paired normal DNA (AUROC=0.9885). Automated, three-fold cross-validation with machine learning (ML) algorithms from the STREAMLINE platform confirmed the highly accurate binary classification by GAPF profiles. The same approach was applied for 15 cfDNA from breast cancer patients and effectively differentiated cancer patients' cfDNA from healthy individuals' cfDNA (AUROC=0.900). These results support the potential of GAPF-seq as a unique, SV-targeting liquid biopsy approach for breast cancer. Citation Format: Fumie Igari, Hisashi Tanaka, Michael M Murata, Mitsue Saito, Armando E Giuliano. Targeting Structural Variants (DNA palindromes) in plasma cell-free DNA for Liquid Biopsy-based Cancer Detection [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr A009.

Radiomics Analysis of Multiparametric PET/MRI for N- and M-Staging in Patients with Primary Cervical Cancer.

The aim of this study was to investigate the potential of multiparametric 18F-FDG PET/MR imaging as a platform for radiomics analysis and machine learning algorithms based on primary cervical cancers to predict N- and M-stage in patients. A total of 30 patients with histopathological confirmation of primary and untreated cervical cancer were prospectively enrolled for a multiparametric 18F-FDG PET/MR examination, comprising a dedicated protocol for imaging of the female pelvis. The primary tumor in the uterine cervix was manually segmented on post-contrast T1-weighted images. Quantitative features were extracted from the segmented tumors using the Radiomic Image Processing Toolbox for the R software environment for statistical computing and graphics. 45 different image features were calculated from non-enhanced as well as post-contrast T1-weighted TSE images, T2-weighted TSE images, the ADC map, the parametric Ktrans, Kep, Ve and iAUC maps and PET images, respectively. Statistical analysis and modeling was performed using Python 3.5 and the scikit-learn software machine learning library for the Python programming language. Prediction of M-stage was superior when compared to N-stage. Prediction of M-stage using SVM with SVM-RFE as feature selection obtained the highest performance providing sensitivity of 91 % and specificity of 92 %. Using receiver operating characteristic (ROC) analysis of the pooled predictions, the area under the curve (AUC) was 0.97. Prediction of N-stage using RBF-SVM with MIFS as feature selection reached sensitivity of 83 %, specificity of 67 % and an AUC of 0.82. M- and N-stage can be predicted based on isolated radiomics analyses of the primary tumor in cervical cancers, thus serving as a template for noninvasive tumor phenotyping and patient stratification using high-dimensional feature vectors extracted from multiparametric PET/MRI data. · Radiomics analysis based on multiparametric PET/MRI enables prediction of the metastatic status of cervical cancers. · Prediction of M-stage is superior to N-stage. · Multiparametric PET/MRI displays a valuable platform forradiomics analyses .

Robust machine−learning based prognostic index using cytotoxic T lymphocyte evasion genes highlights potential therapeutic targets in colorectal cancer

BackgroundA minute fraction of patients stands to derive substantial benefits from immunotherapy, primarily attributable to immune evasion. Our objective was to formulate a predictive signature rooted in genes associated with cytotoxic T lymphocyte evasion (CERGs), with the aim of predicting outcomes and discerning immunotherapeutic response in colorectal cancer (CRC).Methods101 machine learning algorithm combinations were applied to calculate the CERGs prognostic index (CERPI) under the cross−validation framework, and patients with CRC were separated into high− and low−CERPI groups. Relationship between immune cell infiltration levels, immune−related scores, malignant phenotypes and CERPI were further analyzed. Various machine learning methods were used to identify key genes related to both patient survival and immunotherapy benefits. Expression of HOXC6, G0S2, and MX2 was evaluated and the effects of HOXC6 and G0S2 on the viability and migration of a CRC cell line were in−vitro verified.ResultsThe CERPI demonstrated robust prognostic efficacy in predicting the overall survival of CRC patients, establishing itself as an independent predictor of patient outcomes. The low−CERPI group exhibited elevated levels of immune cell infiltration and lower scores for tumor immune dysfunction and exclusion, indicative of a greater potential benefit from immunotherapy. Moreover, there was a positive correlation between CERPI levels and malignant tumor phenotypes, suggesting that heightened CERPI expression contributes to both the occurrence and progression of tumors. Thirteen key genes were identified, and their expression patterns were scrutinized through the analysis of single−cell datasets. Notably, HOXC6, G0S2, and MX2 exhibited upregulation in both CRC cell lines and tissues. Subsequent knockdown experiments targeting G0S2 and HOXC6 resulted in a significant suppression of CRC cell viability and migration.ConclusionWe developed the CERPI for effectively predicting survival and response to immunotherapy in patients, and these results may provide guidance for CRC diagnosis and precise treatment.