Molecular Characteristics Of Cancer Research Articles

The present and future of the Cancer Dependency Map.

Despite tremendous progress in the past decade, the complex and heterogeneous nature of cancer complicates efforts to identify new therapies and therapeutic combinations that achieve durable responses in most patients. Further advances in cancer therapy will rely, in part, on the development of targeted therapeutics matched with the genetic and molecular characteristics of cancer. The Cancer Dependency Map (DepMap) is a large-scale data repository and research platform, aiming to systematically reveal the landscape of cancer vulnerabilities in thousands of genetically and molecularly annotated cancer models. DepMap is used routinely by cancer researchers and translational scientists and has facilitated the identification of several novel and selective therapeutic strategies for multiple cancer types that are being tested in the clinic. However, it is also clear that the current version of DepMap is not yet comprehensive. In this Perspective, we review (1) the impact and current uses of DepMap, (2) the opportunities to enhance DepMap to overcome its current limitations, and (3) the ongoing efforts to further improve and expand DepMap.

Abstract 6115: Genetic ancestry specific meQTLs control immune function regulation in a breast cancer cohort of African and European patients

Abstract Black women with significant African ancestry experience a disproportionately higher incidence of the most aggressive and invasive breast cancer subtypes, along with worse clinical outcomes across all demographic groups within the United States. These health disparities arise from complex interactions between genetic, sociocultural, and environmental factors. Epigenetic mechanisms, such as DNA methylation (DNAm), which regulate gene expression have been proposed as a means to capture the molecular impact of adverse environments on tumor biology. While DNAm levels can be influenced by environmental stimuli, they are also regulated by individual genotypes at methylation quantitative trait loci (meQTLs), posing a challenge to modeling the impact of the environment on cancer molecular characteristics. We hypothesized that ancestry differential DNAm in breast cancer is a result of differential genotype frequencies of SNPs due to ancestry at meQTL loci that can result in phenotypic differences between breast cancer patients of African and European ancestry through differential gene expression regulation. To investigate this hypothesis, we leveraged multi-omic data from The Cancer Genome Atlas (TCGA) breast cancer cohort to perform integrative analysis in patients of European and African ancestry (n=578). 804 CpGs were significantly differentially methylated between European and African ancestry breast cancer patients. meQTL analysis at these sites identified 37 cis-meQTLs and 748 trans-meQTLs, regulating methylation at CpG sites enriched in CpG Islands, enhancers, and other open chromatin regions. 54% of the cis- and 69% of the trans-meQTLs show evidence of differential genotype frequencies by ancestry. Association analysis between methylation and local gene expression revealed that 683 of the 804 ancestry differentially methylated sites regulate the expression of 5,774 genes. We found a statistically significant enrichment for genes involved in immune-related functions such as MHC class II protein complex assembly and the regulation of leukocyte cell-cell adhesion. Intriguingly, 10.5% of these genes encode transcription factors (TFs), and out of these ~23% are linked to African-ancestry specific open chromatin regions in breast cancer cell lines derived from African-ancestry donors. Our findings show that ancestry specific DNAm differences display strong genetic associations via ancestry associated meQTLs, affecting fundamental immunological processes in breast cancer. Our findings suggest that the effects of ancestry-specific genetic regulation may be more profound than previously appreciated, while pointing to the potential for confounding due to genetic ancestry when using DNA methylation as a molecular readout of environmental exposures. Citation Format: Kyriaki Founta, Nyasha Chambwe. Genetic ancestry specific meQTLs control immune function regulation in a breast cancer cohort of African and European patients [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 6115.

Abstract C004: Assessing local ancestry inference using different sequencing assays and depth of coverage

Abstract Introduction: Several studies have reported differences in mutational frequencies between different ancestral populations. For example, PTEN alterations and TMPRSS2-ERG fusions are less common in men with African ancestry. However, these studies have primarily relied on measurements of "global ancestry" and have not been able to ascertain which genomic regions contribute to the observed differences in mutational frequencies. Recent advancements in computational algorithms now enable the estimation of “local ancestry” whereby individual chromosomal segments can be assigned to a particular ancestral background. These tools have been primarily developed for deep whole-genome sequencing (WGS) and have not been optimized for different sequencing assays including ultra-low pass WGS (ULP-WGS), whole exome sequencing (WES), and SNP genotyping arrays. The goal of our study is to establish best practices for local ancestry inference across a diverse set of sequencing and genotyping assays. Methods: We procured WGS data from four ancestries through the 1000 Genomes Project (n=1,067) for local ancestry inference. ULP-WGS and WES (with on- and off-target coverage) were simulated from the full WGS data. Pseudo-array data was emulated by restricting to SNP sites on Affymetrix Genome-Wide Human SNP array 6.0. Each genotype was imputed by Eagle-Beagle (EB) or GLIMPSE2 (GL2), and local ancestry inference was performed using G-nomix using window sizes spanning from 0.2 to 16.0 centimorgans (cM). Accuracy was assessed by comparing local ancestry calls for each assay in a training set (n=1007) to a validation set (n=62). Results: Local ancestry estimation in ULP-WGS data achieved an accuracy exceeding 98% when utilizing GL2, even with the smallest window size of 0.2 cM and a minimum sequencing depth of 0.1x. To achieve similar accuracy of 98% in ULP-WGS with EB, a window size of 1.5 cM was needed for sequencing depths from 2 to 0.5x, with a larger window size of 4 cM required for a depth of 0.25x. Interestingly, such accuracy did not obtain at a depth of 0.1x. For WES with on-target sites and pseudo-array data, we utilized EB for imputation as it was more computationally efficient and accurate with larger window sizes compared to GL2. The accuracy of WES with on-target sites exceeded 90% utilizing 4.0 cM or larger window sizes, but no window size achieved 98% accuracy. Incorporating off-target regions in WES and employing GL2 enhanced the accuracy to 98% at a window size of 1.5 cM. Pseudo-array data with EB achieved an accuracy of 98% at 0.5 cM and thus was better than WES. Conclusions: Our results show that local ancestry can be accurately estimated in different sequencing and genotyping assays when the window size and imputation tool are appropriately selected. This work will facilitate the use of local ancestry inference in studies utilizing different genotyping assays. Ultimately, researchers understand the influence of local ancestral haplotypes on molecular features of cancer. Citation Format: Tomoki Motegi, Joshua D. Campbell. Assessing local ancestry inference using different sequencing assays and depth of coverage [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr C004.

Comprehension of rectosigmoid junction cancer molecular features by comparison to the rectum or sigmoid colon cancer.

Colorectal cancer (CRC) is a heterogeneous cancer. Its treatment depends on its anatomical site and molecular features. Carcinomas of the rectosigmoid junction are frequent; however, specific data on these tumors are sparse, as they are frequently assigned to either the colon or rectum. This study sought to identify the molecular features of rectosigmoid junction cancer to determine whether there should be any difference between the therapeutic management of rectosigmoid junction cancer and that of sigmoid colon or rectum cancer. The data of 96 CRC patients with carcinomas in the sigmoid colon, rectosigmoid junction, and rectum were retrospectively summarized. The next-generation sequencing (NGS) data of the patients were analyzed to study the molecular characteristics of the carcinomas in different locations of the bowel. In total, there was no difference in the clinicopathologic characteristics of the three groups. TP53, APC, and KRAS genes were the top 3 alteration genes in sigmoid colon, rectosigmoid junction, and rectum cancer. The rates of the KRAS, NRAS, and PIK3CA increased as the location moved distally, while the rates of APC and BRAF decreased. Almost no significant molecular differences were found among the three groups. The prevalence of the FLT3, fms-related tyrosine kinase 1 (FLT1), and phosphoenolpyruvate carboxykinase 1 (PCK1) mutation was lower in the rectosigmoid junction group than the sigmoid colon and rectum groups (P>0.05). The proportion of the transforming growth factor beta pathway was higher in the rectosigmoid junction and rectum groups than the sigmoid colon group (39.3% vs. 34.3% vs. 18.2%, respectively, P=0.121, P=0.067, P=0.682); a higher proportion of MYC pathway was also observed in the rectosigmoid junction than that in rectum and sigmoid colon (28.6% vs. 15.2% vs. 17.1%, P=0.278, P=0.202, P=0.171). Regardless of the clustering method employed, the patients were divided into two clusters, and the composition of clusters revealed no significant differences in terms of the different locations. Rectosigmoid junction cancer has a distinctive molecular profile compared to the molecular profiles of the adjacent bowel segment cancers.

Renaming Grade Group 1 Prostate “Cancer” From a Pathology Perspective: A Call for Multidisciplinary Discussion

Despite the innovations made to enhance smarter screening and conservative management for low-grade prostate cancer, overdiagnosis, and overtreatment remains a major health care problem. Driven by the primary goal of reducing harm to the patients, relabeling of nonlethal grade group 1 (GG 1) prostate cancer has been proposed but faced varying degrees of support and objection from clinicians and pathologists. GG 1 tumor exhibits histologic (invasive) and molecular features of cancer but paradoxically, if pure, is unable to metastasize, rarely extends out of the prostate, and if resected, has a cancer-specific survival approaching 100%. Most of the arguments against relabeling GG 1 relate to concerns of missing a higher-grade component through the unsampled area at biopsy. However, the designation of tumor benignity or malignancy should not be based on the shortcomings of a diagnostic procedure and sampling errors. This review explores possible solutions, mainly the feasibility of renaming GG 1 in radical prostatectomy (RP) with ramifications in biopsy diagnosis, acceptable for both pathologists and clinicians. One workable approach is to rename GG 1 in RP with a cautious neutral or nonbenign non-cancer term (eg, acinar neoplasm) using "defined criteria" that will stop the indiscriminate reporting of every GG 1 in biopsy as carcinoma including eventual insignificant microtumors in RPs. Use of a corresponding noncommittal term at biopsy while commenting on the possibility of an undersampled nonindolent cancer, might reduce the pathologist's concerns about upgrading. Dropping the word "carcinoma" in biopsy preempts the negative consequences of labeling the patient with cancer, including unnecessary definitive therapy (the root cause of overtreatment). Renaming should retain the status quo of contemporary grading and risk stratifications for management algorithms while trying to minimize overtreatment. However, the optimal approach to find answers to this issue is through multidisciplinary discussions of key stakeholders with a specific focus on patient-centered concerns and their ramifications in our practices. GG 1 renaming has been brought up in the past and came up again despite the continued counterarguments, and if not addressed more comprehensively will likely continue to reemerge as overdiagnosis, overtreatment, and patient's sufferings persist.

The Role of Organic Cation Transporters in the Pharmacokinetics, Pharmacodynamics and Drug-Drug Interactions of Tyrosine Kinase Inhibitors.

Tyrosine kinase inhibitors (TKIs) decisively contributed in revolutionizing the therapeutic approach to cancer, offering non-invasive, tolerable therapies for a better quality of life. Nonetheless, degree and duration of the response to TKI therapy vary depending on cancer molecular features, the ability of developing resistance to the drug, on pharmacokinetic alterations caused by germline variants and unwanted drug-drug interactions at the level of membrane transporters and metabolizing enzymes. A great deal of approved TKIs are inhibitors of the organic cation transporters (OCTs). A handful are also substrates of them. These transporters are polyspecific and highly expressed in normal epithelia, particularly the intestine, liver and kidney, and are, hence, arguably relevant sites of TKI interactions with other OCT substrates. Moreover, OCTs are often repressed in cancer cells and might contribute to the resistance of cancer cells to TKIs. This article reviews the OCT interactions with approved and in-development TKIs reported in vitro and in vivo and critically discusses the potential clinical ramifications thereof.

Abstract C055: CARM1-mediated BAF155 methylation promotes metastasis and is targetable to reduce disparate mortality in triple negative breast cancer

Abstract Breast cancer (BC) has disparate mortality rates between African American (AA) and non-Hispanic White (White) individuals. Despite an overall lower incidence of BC, AA women are 41% more likely to die from BC than White women. BC can be biochemically classified based on the expression of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2), or classified by gene expression profiling as luminal, HER2+ and basal-like types. A recent large cohort analysis of nearly 200,000 patients found that AA women had a nearly three-fold increased risk of triple-negative BC, a subtype of basal-like BC that is associated with poor prognosis and metastasis. Although an increasing number of studies compare breast cancer molecular features and gene expression differences between breast tumors from AA and White patients, the underlying biological mechanisms contributing to this disparity are still unknown. We studied the oncogenic roles of coactivator associated arginine methyltransferase 1 (CARM1) and that is highly expressed in TNBC and CARM1 was overexpressed in AA patients through TCGA analysis. We have identified BRG1-Associated Factor 155 (BAF155), a component of SWI/SNF chromatin remodeling complex, is specifically methylated by CARM1. Herein, we discovered the dual roles of methylated-BAF155 (me-BAF155) in promoting tumor metastasis: activation of super-enhancer-addicted oncogenes by recruiting BRD4, and repression of interferon α/γ pathway genes to suppress host immune response. Pharmacological inhibition of CARM1 and BAF155 methylation not only abrogated the expression of an array of oncogenes, but also boosted host immune responses by enhancing the activity and tumor infiltration of cytotoxic T cells. Moreover, strong me-BAF155 staining was detected in circulating tumor cells from metastatic cancer patients. Despite low cytotoxicity, CARM1 inhibitors strongly inhibited TNBC cell migration in vitro, and growth and metastasis in vivo. These data suggest that me-BAF155 is a marker for detecting metastasis patients in AA women and CARM1 is an important epigenetic target for treatment of TNBC to reduce mortality in AA women. Citation Format: Eui-Jun Kim, Peng Liu, Yidan Wang, Irene M. Ong, Wei Xu. CARM1-mediated BAF155 methylation promotes metastasis and is targetable to reduce disparate mortality in triple negative breast cancer [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr C055.

Assessment of Spinal Metastases Surgery Risk Stratification Tools in Breast Cancer by Molecular Subtype.

Breast cancer molecular features and modern therapies are not included in spine metastasis prediction algorithms. To examine molecular differences and the impact of postoperative systemic therapy to improve prognosis prediction for spinal metastases surgery and aid surgical decision making. This is a retrospective multi-institutional study of patients who underwent spine surgery for symptomatic breast cancer spine metastases from 2008 to 2021 at the Massachusetts General Hospital and Brigham and Women's Hospital. We studied overall survival, stratified by breast cancer molecular subtype, and calculated hazard ratios (HRs) adjusting for demographics, tumor characteristics, treatments, and laboratory values. We tested the performance of established models (Tokuhashi, Bauer, Skeletal Oncology Research Group, New England Spinal Metastases Score) to predict and compare all-cause. A total of 98 patients surgically treated for breast cancer spine metastases were identified (100% female sex; median age, 56 years [IQR, 36-84 years]). The 1-year probabilities of survival for hormone receptor positive, hormone receptor positive/human epidermal growth factor receptor 2+, human epidermal growth factor receptor 2+, and triple-negative breast cancer were 63% (45 of 71), 83% (10 of 12), 0% (0 of 3), and 12% (1 of 8), respectively ( P < .001). Patients with triple-negative breast cancer had a higher proportion of visceral metastases, brain metastases, and poor physical activity at baseline. Postoperative chemotherapy and endocrine therapy were associated with prolonged survival. The Skeletal Oncology Research Group prognostic model had the highest discrimination (area under the receiver operating characteristic, 0.77 [95% CI, 0.73-0.81]). The performance of all prognostic scores improved when preoperative molecular data and postoperative systemic treatment plans was considered. Spine metastases risk tools were able to predict prognosis at a significantly higher degree after accounting for molecular features which guide treatment response.

Evaluating a therapeutic window for precision medicine by integrating genomic profiles and p53 network dynamics

The response variation to anti-cancer drugs originates from complex intracellular network dynamics of cancer. Such dynamic networks present challenges to determining optimal drug targets and stratifying cancer patients for precision medicine, although several cancer genome studies provided insights into the molecular characteristics of cancer. Here, we introduce a network dynamics-based approach based on attractor landscape analysis to evaluate the therapeutic window of a drug from cancer signaling networks combined with genomic profiles. This approach allows for effective screening of drug targets to explore potential target combinations for enhancing the therapeutic window of drug responses. We also effectively stratify patients into desired/undesired response groups using critical genomic determinants, which are network-specific origins of variability to drug response, and their dominance relationship. Our methods provide a viable and quantitative framework to connect genotype information to the phenotypes of drug response with regard to network dynamics determining the therapeutic window.

Multifactorial drug response modeling based on cancer organoid data.

e13544 Background: Prediction of drug response based on cancer molecular profiles is of paramount importance for precision oncology. Most existing drug response prediction models are built using drug screening data of immortalized cancer cell lines, which usually have altered genomic profiles compared with patient tumors. Recently, patient-derived organoids (PDOs) are emerging as a promising platform better representing patient tumors. We build computational drug response prediction models based on PDO drug screening data, which is the first study of its type to our knowledge. Methods: We successfully developed 27 PDO lines of colorectal cancer and 20 PDO lines of head and neck (H&amp;N) cancer. Transcriptomics, copy number variation (CNV), and targeted DNA mutation data were generated for the PDO lines. The PDO lines were screened with 36 drugs of diversified mechanisms. The area under the dose response curve was taken as the response measurement. We used the LightGBM algorithm to build response prediction models based on cancer molecular data and drug chemical descriptors/fingerprints. To investigate the influence of different factors on the prediction performance, including different cancer types, cancer molecular features, drug features, data preprocessing methods, and others, we applied a multifactorial analysis scenario to build and evaluate 3,384 prediction models constructed with all possible combinations of the factors. For example, we built prediction models for H&amp;N and colorectal PDOs separately and jointly. Results: A prediction model built for H&amp;N PDOs achieved the highest prediction performance among all prediction models, which was R2 of 0.790 in 10-fold cross-validation. The model was built using drug descriptors, CNVs, and expressions of “landmark” genes well-representing cellular transcriptomic changes identified in the LINCS project. The table below includes all the factorial differences that caused an average R2 change larger than 1%. All R2 changes are statistically significant (p-values &lt; 1×10–50), evaluated by pair-wise t-tests comparing models built with the status of the factor changed. The prediction performance increased, from colorectal cancer to two cancer types combined, and to H&amp;N cancer. Gene expression data, either whole-transcriptome or the subset of LINCS genes, boosted the prediction performance. Between the two different dyes used to stain dead cells, TO-PRO-3 provided a higher prediction performance than Caspase-3/7. Conclusions: The highest drug response prediction performance achieved is R2 of 0.790. Cancer type, dye, and whether gene expressions are used in modeling are the factors most influential on prediction performance.[Table: see text]

Comprehensive profiling of the molecular characteristics of both primary tumor and local metastases tumor in Chinese lung cancers.

e20517 Background: Metastasis tumor is the leading cause of lung cancer-related deaths, including local metastasis and distant metastasis. Despite its crucial clinical significance, local metastasis of lung cancer is still largely uncharacterized in terms of underlying molecular mechanisms. Here, we performed TSO500 (Illumina, San Diego, CA, USA) sequencing with 19 matched lung cancer primary and local metastatic samples to comprehensively profiling of lung cancer molecular characteristics. Methods: 19 lung cancer patients with paired primary tumor and local metastatic samples were enrolled, including 16 matched primary-lymph node samples and 3 primary-pleura samples. We compared molecular characteristics, including SNV and TMB, of matched samples. Molecular alterations identified were classified into shared (mutations in both primary and metastases) and private (mutations only in primary or metastases) groups. Thus, share mutation statistical analysis, pathway level enrichment and mutational signature was also done. Results: Comprehensively analysis of matched samples showed that mutational concordance between primary tumor and local metastases is lower. The average rate of concordance of primary-lymph node and primary-pleura was 40.6%±28.4%, 39.0%±33.9% respectively. Obviously, no difference in TMB between local metastases and primary tumor was found, average of primary vs lymph node and primary vs pleura was 7.5 vs 6.5, 6.9 vs 4.2 respectively. Significantly, we found that smoking lung cancer patients have higher TMB compared to non-smokers(p &lt; 0.01), regardless primary tumor or metastatic lymph nodes. Conclusions: Lung cancer has higher inter-tumor heterogeneity with proportion of shared mutations less than 50%. Meanwhile, smoking lung cancer has higher TMB in both primary tumor and metastatic lymph nodes compared to non-smokers.

The Analysis of Platelet-Derived circRNA Repertoire as Potential Diagnostic Biomarker for Non-Small Cell Lung Cancer.

Simple SummaryInteraction between blood platelets and cancer cells play an important role in various steps of cancer development and progression. These interactions lead to changes in the platelets’ RNA content, resulting in tumor-mediated “education” of platelets. Tumor-educated platelets (TEPs) can be used as a non-invasive biomarker source for cancer detection and progression monitoring. Our lab has previously identified that spliced mRNA TEP signatures provide specific information on the presence, location, and molecular features of cancers. Next to mRNA, other RNA types are present in platelets, and their repertoire can potentially be subjected to cancer-mediated alterations. Despite the evidence that circRNA could be a promising cancer biomarker, they have not yet been analyzed in blood platelets of cancer patients. In this proof-of-concept study, we aim to evaluate whether platelets’ circRNA signature could be used as a biomarker for cancer detection and progression.Tumor-educated Platelets (TEPs) have emerged as rich biosources of cancer-related RNA profiles in liquid biopsies applicable for cancer detection. Although human blood platelets have been found to be enriched in circular RNA (circRNA), no studies have investigated the potential of circRNA as platelet-derived biomarkers for cancer. In this proof-of-concept study, we examine whether the circRNA signature of blood platelets can be used as a liquid biopsy biomarker for the detection of non-small cell lung cancer (NSCLC). We analyzed the total RNA, extracted from the platelet samples collected from NSCLC patients and asymptomatic individuals, using RNA sequencing (RNA-Seq). Identification and quantification of known and novel circRNAs were performed using the accurate CircRNA finder suite (ACFS), followed by the differential transcript expression analysis using a modified version of our thromboSeq software. Out of 4732 detected circRNAs, we identified 411 circRNAs that are significantly (p-value < 0.05) differentially expressed between asymptomatic individuals and NSCLC patients. Using the false discovery rate (FDR) of 0.05 as cutoff, we selected the nuclear receptor-interacting protein 1 (NRIP1) circRNA (circNRIP1) as a potential biomarker candidate for further validation by reverse transcription–quantitative PCR (RT-qPCR). This analysis was performed on an independent cohort of platelet samples. The RT-qPCR results confirmed the RNA-Seq data analysis, with significant downregulation of circNRIP1 in platelets derived from NSCLC patients. Our findings suggest that circRNAs found in blood platelets may hold diagnostic biomarkers potential for the detection of NSCLC using liquid biopsies.

Physiologic and molecular characterization of a novel murine model of metastatic head and neck cancer cachexia.

BackgroundCancer cachexia is a metabolic disorder characterized by the progressive loss of fat and lean mass that results in significant wasting, ultimately leading to reduced quality of life and increased mortality. Effective therapies for cachexia are lacking, potentially owing to the mismatch in clinically relevant models of cachexia. Specifically, cachexia observed in a clinical setting is commonly associated with advanced or late‐stage cancers that are metastatic, yet pre‐clinical metastatic models of cachexia are limited. Furthermore, the prevalence of cachexia in head and neck cancer patients is high, yet few pre‐clinical models of head and neck cancer cachexia exist. In addition to these shortcomings, cachexia is also heterogeneous among any given cancer, whereas patients with similar disease burden may experience significantly different degrees of cachexia symptoms. In order to address these issues, we characterize a metastatic model of human papilloma virus (HPV) positive head and neck squamous cell carcinoma that recapitulates the cardinal clinical and molecular features of cancer cachexia.MethodsMale and female C57BL/6 mice were implanted subcutaneously with oropharyngeal squamous cell carcinoma cells stably transformed with HPV16 E6 and E7 together with hRas and luciferase (mEERL) that metastasizes to the lungs (MLM). We then robustly characterize the physiologic, behavioural, and molecular signatures during tumour development in two MLM subclones.ResultsMice injected with MLM tumour cells rapidly developed primary tumours and eventual metastatic lesions to the lungs. MLM3, but not MLM5, engrafted mice progressively lost fat and lean mass during tumour development despite the absence of anorexia (P < 0.05). Behaviourally, MLM3‐implanted mice displayed decreased locomotor behaviours and impaired nest building (P < 0.05). Muscle catabolism programmes associated with cachexia, including E3 ubiquitin ligase and autophagy up‐regulation, along with progressive adipose wasting and accompanying browning gene signatures, were observed. Tumour progression also corresponded with hypothalamic and peripheral organ inflammation, as well as an elevation in neutrophil‐to‐lymphocyte ratio (P < 0.05). Finally, we characterize the fat and lean mass sparing effects of voluntary wheel running on MLM3 cachexia (P < 0.05).ConclusionsThis syngeneic MLM3 allograft model of metastatic cancer cachexia is reliable, consistent, and readily recapitulates key clinical and molecular features and heterogeneity of cancer cachexia. Because few metastatic models of cachexia exist—even though cachexia often accompanies metastatic progression—we believe this model more accurately captures cancer cachexia observed in a clinical setting and thus is well suited for future mechanistic studies and pre‐clinical therapy development for this crippling metabolic disorder.

Abstract 862: Transcriptomics analysis reveals unique immunoinflammatory signatures of nigerian prostate cancer

Abstract Background: Prostate cancer (PCa) is the leading cause of cancer death among Nigerian men, with most men presenting late and with high grade disease. Furthermore, improved PCa diagnosis and treatment could rely on genomics-driven precision approaches. Mounting evidence supports potential population enriched molecular features of cancer, however the extent of which is hampered due to a dearth of genomic data in diverse cohorts. This study, therefore, aims at utilizing transcriptomics analysis to deduce transcriptional states in PCa from Nigerian men. Methodology: RNA was extracted from macro-dissected prostate tumor and adjacent normal tissue from 35 archival formalin fixed paraffin-embedded (FFPE) blocks from Gleason 8-9 cancers, of which 25 had cribriform morphologic architecture. High resolution hybrid-capture mRNA-seq libraries were sequenced using Illumina NovaSeq 6000 system and generated an average of 124 million read pairs per sample. Raw reads were processed using a custom pipeline based upon industry standard algorithms along with the deduction of genetic ancestry. Molecular annotation was also performed to investigate tumor and immune cell heterogeneity. Result: The Nigerian data were compared with high grade PCa data of Caucasian obtained from TCGA. The Nigerian PCa tumors where characterized with uniquely enriched immune-inflammatory microenvironment which include crosstalk between dendritic cells and natural killer cells, and natural killer cell signaling. While the Nigerian tumors share more features of distal metastatic tumor, their enrichment in NKT Cells is a unique feature. Conclusion: This represents the first report of RNA-seq analysis of PCa in Sub-Saharan Africa and the unique transcriptomics features of Nigerian PCa presents opportunities for precision intervention in addressing its burden in Nigeria. Citation Format: Solomon Oladapo Rotimi, Abidemi E. Omonisi, Faruk Mohammed, Paul Jibrin, Babatunde Duduyemi, Daniel B. Olusina, Yili Xu, Yuxin Jin, Xiyu Liu, Guang Q. Xiao, Folakemi Odedina, John Carpten, Renee Reams, Zarko Manojlovic. Transcriptomics analysis reveals unique immunoinflammatory signatures of nigerian prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 862.

Tailored Therapy Based on Molecular Characteristics in Endometrial Cancer.

Management of endometrial cancer, an adenocarcinoma of the endometrium which occupies most uterine corpus neoplasms, including uterine sarcomas, has been more relevant due to its increasing incidence. Extensive research on tumorigenesis molecular mechanisms and molecular characterization across cancers has brought paradigm shifts in the treatment of various malignant tumors. Endometrial cancer treatment has been traditionally guided according to the disease extent or histology types, while recent studies on molecular features have led to the introduction of targeted agents into clinical use, along with conventional chemotherapeutic agents in patients with recurrent or metastatic disease. Considering the proven efficacy and relatively tolerable toxicities of targeted therapies across malignant tumors, improvement of treatment outcomes is also expected in endometrial cancer by adopting an individualized therapy depending on the specific molecular features. Efficacy assessment of new biological agents is still ongoing based on previous preclinical data on endometrial cancer molecular features. Here, endometrial cancer molecular characterization will be reviewed, and then, we will introduce preclinical data, directing the adoption of new biological agents.

Abstract PS7-12: Impact of race/ethnicity on triple negative breast cancer molecular features, treatment response and clinical outcomes in patients receiving neoadjuvant therapy

Abstract Background: Triple negative breast cancer (TNBC) disproportionally impacts Black and Hispanic women and is suggested to be partly responsible for disparities observed in breast cancer mortality. We sought to evaluate the impact of race/ethnicity on TNBC molecular subtype, immune profile, pathologic complete response (pCR) and recurrence free survival (RFS). Methods: The ARTEMIS trial (NCT02276443) uses imaging response and molecular profiling to personalize neoadjuvant chemotherapy in early stage TNBC. After 4 cycles of AC, patients with chemo-sensitive disease receive standard taxane-based therapy, while those with chemo-resistant disease are offered therapeutic trials based upon molecular profiling. Pathologic response was assessed at surgery. Patients self-reported race/ethnicity was categorized as Asian, Black/African-American, Hispanic/Latino and White (non-Hispanic). Patients were excluded if no race or ethnicity was reported. Clinical and pathologic factors were recorded and compared. Gene expression profiling was performed by RNAseq to determine Vanderbilt signature. PD-L1 and Androgen Receptor (AR) were determined by immunohistochemistry. Frequencies at which various factors were observed by race/ethnicity were calculated and Fisher’s exact test performed to determine statistical significance. Kaplan-Meier analysis was used to estimate survival stratified by pCR status and race/ethnicity. Results: Among 321 women enrolled in ARTEMIS, 26 (8.1%) were classified as Asian, 50 (15.6%) as Black, 59 (18.4%) as Hispanic and 186 (57.9%) as White. Demographic and clinical features were similar, except Black women were more likely to have BMI ≥30 (70.0%, p&amp;lt;0.001). Comparing by race/ethnicity, there was no statistically significant difference in tumor histology, tumor size, nodal status, clinical stage, chemo-resistant disease, type of surgery or receipt of radiation. A trend toward higher proportion of N3 disease was observed in Black women (24.0%) compared to other groups (p=0.054). Tumor profiling results are listed in Table 1. No statistically significant difference was observed by race/ethnicity in Vanderbilt signature, presence of PD-L1 or stromal tumor infiltrating lymphocytes (sTIL) status. More AR-positive tumors were identified in Asian women (64%, p=0.007). pCR and RCB status did not significantly vary by race/ethnicity however, higher proportion of RCB-III disease was identified in Black (14%) and Hispanic women (16.9%) as compared to Asian (3.8%) and White (8.6%) women (p=0.164). At median follow up of 23.8 months (range 3.4-51.1), there was no statistically significant difference in RFS by race/ethnicity. Conclusion: Among this population of patients with TNBC treated with neoadjuvant chemotherapy there was no statistically significant difference observed in TNBC Vanderbilt signature, PD-L1 staining, sTIL, pCR or RFS by race/ethnicity. Asian women were found to have a higher incidence of AR-positive subtype. Numerically higher rates of N3 stage in Black women and RCB-III classification in Black and Hispanic women were identified, however not statistically significant. Larger cohorts of patients are needed to further investigate these findings. Table 1. TNBC profiling compared by race/ethnicity.Total n (%)Asian n (%)Black n (%)Hispanic/Latino n (%)White n (%)p valueVanderbilt signature n (%) Basal like (BL1) Basal like 2 (BL2) Immunomodulatory (IM) Luminal androgen receptor (LAR) Mesenchymal (M) Mesenchymal stem-like (MSL) Unstable (UNS)231 50 (21.6) 25 (10.8) 42 (18.2) 22 (9.5) 48 (20.8) 14 (6.0) 30 (13.0)21 (9.1) 0 (0.0) 4 (19.0) 5 (23.8) 3 (14.3) 5 (23.8) 1 (4.8) 3 (14.3)35 (15.2) 9 (25.7) 5 (14.3) 6 (17.1) 2 (5.7) 7 (20.0) 1 (2.9) 5 (14.3)43 (18.6) 14 (32.5) 3 (7.0) 10 (23.2) 5 (11.6) 6 (14.0) 2 (4.7) 3 (7.0)132 (57.1) 27 (20.5) 13 (9.8) 21 (15.9) 12 (9.1) 30 (22.7) 10 (7.6) 19 (14.4)NSAndrogen Receptor n (%) Positive (&amp;gt;=10%) Negative (&amp;lt;10%)311 109 (35.0) 202 (65.0)25 (8.0) 16 (64.0) 9 (36.0)49 (15.8) 11 (22.4) 38 (77.6)57 (18.3) 19 (33.3) 38 (66.7)180 (57.9) 63 (35.0) 117 (65.0)0.007PD-L1 n (%) None &amp;gt;1298 212 (71.1) 86 (28.9)24 (8.1) 15 (62.5) 9 (37.5)47 (15.8) 33 (70.2) 14 (29.8)57 (19.1) 40 (70.2) 17 (29.8)170 (57.0) 124 (72.9) 46 (27.1)NSStromal tumor infiltrating lymphocytes (sTIL) n (%) High (&amp;gt;=20%) Low (&amp;lt;20%)319 107 (33.5) 212 (66.5)26 (8.1) 12 (46.2) 14 (53.8)50 (15.7) 13 (26.0) 37 (74.0)58 (18.2) 21 (36.2) 37 (63.8)185 (58.0) 61 (33.0) 124 (67.0)NS Citation Format: Mediget Teshome, Ryan Sun, Elizabeth E. Ravenberg, Abenaa Brewster, Mariana Chavez-MacGregor, Jason B. White, Stacy Moulder. Impact of race/ethnicity on triple negative breast cancer molecular features, treatment response and clinical outcomes in patients receiving neoadjuvant therapy [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS7-12.

Abstract 5468: Gene fusions in breast cancer in Nigerian women

Abstract Background: As a chromosomal rearrangement event, gene fusion plays a critical role in the pathogenesis of cancer by creating potentially oncogenic chimeric proteins. However, gene fusions have been understudied in breast cancer patients of African ancestry, who are often diagnosed at a younger age and with more aggressive molecular features of cancer than patients of other races/ethnicities. Methods: Ninety-six women diagnosed with invasive breast cancer were recruited from Ibadan, Nigeria with mean age at diagnosis of 51.6 ± 12.4 years. Primary tumors were collected, of which 62 (64.6%) were hormone receptor negative (HR-) by immunohistochemistry, and 31 (32.3%) were basal-like subtype by PAM50 classification. Paired-end reads from RNA-seq on these tumors were used for gene fusion detection by three programs, STAR-Fusion, STAR-SEQR, and Arriba. To increase specificity, we applied an ensembling method by selecting fusions identified by at least two of the three callers. Multiple filters were applied to fusion candidates to remove likely false positives, including fusions containing genes of mitochondrial origin, fusions consisting of pairs of paralogues or orthologs, fusions involving HLA genes, and fusions found in several databases of non-cancer tissues. To investigate potentially druggable fusion transcripts, we compared our call set with the OncoKB precision oncology knowledge base. Results: STAR-Fusion, STAR-SEQR, and Arriba identified 682, 4529, and 3056 unique fusion transcripts, respectively. Following application of the ensembling method and filtering to select final fusions, 709 unique fusions were identified. Comparison with 13 databases and published papers identified 62 of these fusions as previously reported. The mean fusion burden per sample was 7.7 ± 7.4. The fusion burden per sample was significantly smaller for tumors classified as Luminal A subtypes by PAM50 classification than Luminal B, Basal, and HER2 subtypes (P &amp;lt; 0.03) as well as normal like (P &amp;lt; 0.01). Fusion burden was highest in HER2 tumors (NS), consistent with previous reports. The number of fusion transcripts per sample did not differ significantly according to the patient's age. Ninety-six percent of fusion transcripts were only identified in a single sample, including the ETV6-NTRK3 fusion, previously reported in secretory breast carcinoma, and BCR-ABL1, both of which are targeted by a drug identified in OncoKB. The most commonly observed fusion, EDDM13–ZNF71, appeared in 4.2% (4/96) of samples. Conclusion: The vast majority of breast cancer samples from Nigerian women demonstrate unique fusion transcripts in expressed RNA. Fusion burden per sample was related to PAM50 classification. Future work will incorporate functional studies of the recurrent gene fusion events identified in our cohort. We will additionally focus on validation and refinement of our approach to detecting gene fusions in this understudied population, in order to better identify patients who can benefit from new therapies. Citation Format: Anna Elizabeth Woodard, Toshio F. Yoshimatsu, WABCS Working Group, Jason J. Pitt, Yonglan Zheng, Olufunmilayo I. Olopade. Gene fusions in breast cancer in Nigerian women [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5468.

The transcriptional landscape of lncRNAs reveals the oncogenic function of LINC00511 in ER-negative breast cancer

Advances in the molecular characteristics of cancers have facilitated the classification system from morphology to molecular characteristic-based subtypes. Cancer profiling has expanded in its focus from protein-coding genes to noncoding RNAs, with advances in the depth and quality of transcriptome sequencing. Here, we examined the profiles of long noncoding RNAs (lncRNAs) according to breast cancer subtype categories in The Cancer Genome Atlas (TCGA) database to identify a cohort of breast cancer- and oestrogen receptor (ER)-negative-associated lncRNAs. According to the prioritization of variation in ER-negative-associated lncRNAs, we identified and investigated the role of LINC00511 in breast cancer. We determined that high LINC00511 expression was an unfavourable prognostic factor for patients with breast cancer. Furthermore, LINC00511 promoted tumour growth by accelerating the G1/S transition and inhibiting apoptosis. At the transcriptional level, ER deficiency directly affected the expression of LINC00511 activated by transcription factor AP-2 (TFAP-2) in breast cancer cells. Moreover, mechanistic investigations demonstrated that ER-negative-associated LINC00511 interacted with enhancer of zeste homologue 2 (EZH2, the catalytic subunit of polycomb repressive complex 2, PRC2) and recruited PRC2 to mediate histone methylation, contributing to the repression of CDKN1B in the nucleus. This process resulted in altered ER-negative breast cancer cell biology. By highlighting the oncogenic function of LINC00511, we revealed the role of lncRNAs in regulating the network of cell cycle control in ER-negative breast cancer and suggested the exploitation of LINC00511 as an anticancer therapy in the future.

Abstract 4239: Development of pan-cancer transcriptional signatures that predict chemosensitivity

Abstract Background: Despite increasing understanding of the molecular characteristics of cancer, chemotherapy success rates remain low for many cancer types. Studies have attempted to identify patient and tumor characteristics that predict sensitivity or resistance to different types of conventional chemotherapies, yet a concise model that predicts chemosensitivity based on gene expression signatures across cancer types remains to be formulated. We attempted to generate a pan-cancer chemosensitivity predictive model using publicly available data from multiple sources. Such a model may increase the likelihood of identifying the type of chemotherapy most likely to succeed for a given patient based on the gene expression signature of their tumor. Methods: Data used to build predictive models were obtained from the Genomics of Drug Sensitivity in Cancer (GDSC) database, consisting of gene expression profiles from 962 cancer cell lines via RNA sequencing (RNA-seq) and drug sensitivity profiles reported as ln(IC50). Predictive gene signatures were generated using a cross-validated generalized linear model using elastic net penalization parameters. Models were generated for individual chemotherapy drugs, and accuracy of the models was determined by multi-class area under the curve (AUC). Models were then validated using publicly available data from Cancer Cell Line Encyclopedia (CCLE) and NCI-60 as well as publicly available human tumor datasets such as The Cancer Genome Atlas (TCGA). As the training data used to generate the models were from RNA-seq and some of the validation data were generated using microarray technology, feature-specific quantile normalization was used to enable cross-platform analyses. Results: We developed pan-cancer models to predict chemosensitivity to 8 individual chemotherapy drugs from GDSC data. For most models, multi-class AUC ranged from 0.63 to 0.68 when considering all available cancer types. Other models, specifically for etoposide and methotrexate, showed poor performance overall (AUC 0.54-0.55) but performed well when predicting chemosensitivity for bone cancers (AUC 0.70-0.73), which proved difficult with models for other drugs. When considering how well the models predicted chemosensitivity within cancer types, accuracy was improved in some cancer types (e.g., thyroid cancer), with more heterogeneous cancer types (e.g., breast cancer) giving less accuracy. Conclusions: Our results show that these models can predict chemosensitivity across cancer types with clinically useful levels of accuracy, with some cancer types resulting in a high rate of accuracy across several classes of chemotherapy. The inclusion of future clinical datasets, particularly from those cancer types in which chemosensitivity has been difficult to predict, may provide opportunities to strengthen model accuracy as well as decrease the numbers of genes needed to assess chemosensitivity. Citation Format: Jason D. Wells, Todd W. Miller. Development of pan-cancer transcriptional signatures that predict chemosensitivity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4239.

Precision diagnostics based on machine learning-derived imaging signatures

The complexity of modern multi-parametric MRI has increasingly challenged conventional interpretations of such images. Machine learning has emerged as a powerful approach to integrating diverse and complex imaging data into signatures of diagnostic and predictive value. It has also allowed us to progress from group comparisons to imaging biomarkers that offer value on an individual basis. We review several directions of research around this topic, emphasizing the use of machine learning in personalized predictions of clinical outcome, in breaking down broad umbrella diagnostic categories into more detailed and precise subtypes, and in non-invasively estimating cancer molecular characteristics. These methods and studies contribute to the field of precision medicine, by introducing more specific diagnostic and predictive biomarkers of clinical outcome, therefore pointing to better matching of treatments to patients.