Abstract 3139: Study of tumor heterogeneity and subclonality in primary pancreatic and metastatic sites from rapid autopsy patients in PDMR

e13506 Background: The National Cancer Institute has developed a repository of preclinical models [Patient-Derived Models Repository (NCI PDMR, https://pdmr.cancer.gov )] including patient derived xenografts (PDXs), organoids (PDOrgs) and in vitro tumor cultures (PDCs) from patients with solid tumor cancer histologies. A subset of these preclinical models is derived from post-mortem collections from rapid autopsies representing the end point in disease progression. Clinical annotations and genomic datasets associated with these models provide a unique opportunity to study tumor evolution, mechanistic insights into the metastatic process, and treatment resistance. Methods: To date, 43 PDXs, 21 PDCs, and 23 PDOrgs using rapid autopsy specimens from 8 primary and 35 metastatic sites of 18 patients have been developed by the Biological Testing Branch (DTP, DCTD, NCI Frederick, MD) for the PDMR. Whole exome (WES) and total transcriptome (RNASeq) data were processed to generate mutation, copy number alteration (CNA) and gene expression data. Multi-model lineage trees were reconstructed based on putative somatic variants for all the models derived from all patients. The fraction of the genome affected by CNA was compared both within and across PDX models. Results: Most of the rapid autopsy PDX models (32/43) are derived from pancreatic adenocarcinoma (PAAD) patients (13/18), with metastatic specimens originating from sites including liver, colon, omentum, and lung. Driver mutations are present in all preclinical model specimens derived from the same patient. For instance, KRAS p.G12D is present in all patient-derived model specimens derived from PAAD patient 521955. The fraction of the genome affected by CNA remains stable within a PDX model across passages (n = 24, mean = 6.39%, sd = 5.90%). However, we found that this increased when comparing PDX models derived from metastatic sites versus the primary site (n = 19, mean = 16.92%, sd = 10.46%). This indicates presence of tumor heterogeneity between metastatic and primary sites. The lineage tree for models from patient 521955 indicates that one liver metastasis has a unique seeding event compared to the other 4 metastatic sites. Unsupervised clustering analysis on gene expression data also confirms the observed tumor site relationships. Conclusions: Our data demonstrate the potential use of these preclinical models available from the NCI PDMR. These models provide a unique resource for preclinical studies in tumor evolution, metastatic spread mediators, and drug resistance.

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Computed Tomography Measures of Inter-site tumor Heterogeneity for Classifying Outcomes in High-Grade Serous Ovarian Carcinoma: a Retrospective Study

10.1101/531046 ◽

2019 ◽

Cited By ~ 3

Author(s):

Harini Veeraraghavan ◽

H. Alberto Vargas ◽

Alejandro-Jimenez Sanchez ◽

Maura Miccó ◽

Eralda Mema ◽

...

Keyword(s):

Tumor Heterogeneity ◽

Multivariable Analysis ◽

Progression Free Survival ◽

Standard Of Care ◽

Tumour Volume ◽

Ct Imaging ◽

List Type ◽

Ct Image ◽

Non Invasive ◽

Metastatic Sites

AbstractBackgroundHigh grade serous ovarian carcinoma shows marked intra-tumoral heterogeneity which is associated with decreased survival and resistance to platinum-based chemotherapy. Pre-treatment quantification of spatial tumor heterogeneity by multiple tissue sampling is not clinically feasible. Using standard-of-care CT imaging to non-invasively quantify heterogeneity could have high clinical utility and would be highly cost-effective. Texture analysis measures local variations in computed tomography (CT) image intensity. Haralick texture methods are typically used to capture the heterogeneity of entire lesions; however, this neglects the possible presence of texture habitats within the lesion, and the differences between metastatic sites. The primary aim of this study was to develop texture analysis of intra-site and inter-site spatial heterogeneity from standard-of-care CT images and to correlate these measures with clinical and genomic features in patients with HGSOC.Methods and findingsWe analyzed the data from a retrospective cohort of 84 patients with HGSOC consisting of 46 patients from Memorial Sloan Kettering Cancer Center (MSKCC) and 38 non-MSKCC cases selected from The Cancer Imaging Archive (TCIA). Inclusion criteria consisted of FIGO stage II–IV HGSOC, attempted primary cytoreductive surgery, intravenous contrast-enhanced CT of abdomen and pelvis performed prior to surgery and availability of molecular tumor data analysed as per the Cancer Genome Atlas (TCGA) Research Network ovarian cancer project. Manual segmentation and image analysis was performed on 463 metastatic tumor sites from 84 patients. In the MSKCC cohort the median number of tumor sites was 7 (interquartile range 5–9) and 4 (interquartile range 3–4) in the TCIA patients. Sub-regions were produced within each tumor site by grouping voxels with similar Haralick texture using the Kernel K-means method. We derived statistical measures of intra- and inter-site tumor heterogeneity (IISTH) including cluster sites entropy (cSE), cluster sites standard deviation (cluDev) and cluster sites dissimilarity (cluDiss) from sub-regions identified within and between individual tumor sites. Unsupervised clustering was used to group patient IISTH measures into low, medium, high, and ultra-high heterogeneity clusters from each cohort.The IISTH measure cluDiss was an independent predictor of progression-free survival (PFS) in multivariable analysis in both datasets (MSKCC hazard ratio [HR] 1.04, 95% CI 1.01–1.06, P = 0.002; TCIA HR 1.05, 95% CI 1.00–1.10, P = 0.049). Low and medium IISTH clusters were associated with longer PFS in multivariable analysis (MSKCC HR 2.94, 90% CI 1.29–6.70, P = 0.009, TCIA HR 5.94, 95% CI 1.05–33.6, P = 0.044). IISTH measures were robust to differences in the CT imaging systems. Average Haralick textures contrast (TCIA HR 1.08, 95% CI 1.01–1.10, P = 0.019) and homogeneity (TCIA HR 1.09, 95% CI 1.02–1.16, P = 0.008) were associated with PFS in mutivariate analysis only in the TCIA dataset. All other average Haralick textures and total tumor volume were not associated with PFS in either dataset.ConclusionsTexture measures of intra- and inter-site tumor heterogeneity from standard of care CT images are correlated with shorter PFS in HGSOC patients. These quantitative methods are independent of the CT imaging system and can thus be applied in clinical practice. The methodology proposed here enables the non-invasive quantification of intra-tumoral heterogeneity and disease stratification for future experimental medicine studies and clinical trials, particularly in cases where total tumour volume and averaged textures have low predictive power.Author summaryWhy was this study done?Tumor heterogeneity is a feature of many solid malignancies including ovarian cancer.Recent genomic research suggests that intra-site tumor heterogeneity (heterogeneity within a single tumor site) and inter-site tumor heterogeneity (heterogeneity between different metastatic sites in the same patient) correlate with clinical outcome in HGSOC.What did the researchers do and find?We developed quantitative and non-invasive image-analysis based measures for predicting outcome in HGSOC patients by combining image-based information from within and between multiple tumor sites.Using datasets from two sources, we demonstrate that these image-based tumor heterogeneity measures predict progression free survival in patients with HGSOC.What do these findings mean?Non-invasive measures of CT image heterogeneity may predict outcomes in HGSOC patients.Wider application of these CT image heterogeneity measures could prove useful for stratifying patients to different therapies given that total tumour volume and averaged textures have low predictive power.

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Breast Cancer Heterogeneity

Diagnostics ◽

10.3390/diagnostics11091555 ◽

2021 ◽

Vol 11 (9) ◽

pp. 1555

Author(s):

Caterina Fumagalli ◽

Massimo Barberis

Keyword(s):

Breast Cancer ◽

Tumor Progression ◽

Tumor Heterogeneity ◽

Therapeutic Strategy ◽

Recurrent Disease ◽

Breast Cancer Patients ◽

Cancer Heterogeneity ◽

Non Invasive ◽

Effective Therapeutic Strategy ◽

Metastatic Sites

Breast tumor heterogeneity is a major challenge in the clinical management of breast cancer patients. Both inter-tumor and intra-tumor heterogeneity imply that each breast cancer (BC) could have different prognosis and would benefit from specific therapy. Breast cancer is a dynamic entity, changing during tumor progression and metastatization and this poses fundamental issues to the feasibility of a personalized medicine approach. The most effective therapeutic strategy for patients with recurrent disease should be assessed evaluating biopsies obtained from metastatic sites. Furthermore, the tumor progression and the treatment response should be strictly followed and radiogenomics and liquid biopsy might be valuable tools to assess BC heterogeneity in a non-invasive way.

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Novel temporal and spatial patterns of metastatic colonization from breast cancer rapid-autopsy tumor biopsies

Genome Medicine ◽

10.1186/s13073-021-00989-6 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Xiaomeng Huang ◽

Yi Qiao ◽

Samuel W. Brady ◽

Rachel E. Factor ◽

Erinn Downs-Kelly ◽

...

Keyword(s):

Breast Cancer ◽

Metastatic Breast Cancer ◽

Critical Role ◽

Metastatic Breast ◽

Clinical Intervention ◽

Metastatic Spread ◽

Tumor Evolution ◽

Metastatic Colonization ◽

Rapid Autopsy ◽

Metastatic Sites

Abstract Background Metastatic breast cancer is a deadly disease with a low 5-year survival rate. Tracking metastatic spread in living patients is difficult and thus poorly understood. Methods Via rapid autopsy, we have collected 30 tumor samples over 3 timepoints and across 8 organs from a triple-negative metastatic breast cancer patient. The large number of sites sampled, together with deep whole-genome sequencing and advanced computational analysis, allowed us to comprehensively reconstruct the tumor’s evolution at subclonal resolution. Results The most unique, previously unreported aspect of the tumor’s evolution that we observed in this patient was the presence of “subclone incubators,” defined as metastatic sites where substantial tumor evolution occurs before colonization of additional sites and organs by subclones that initially evolved at the incubator site. Overall, we identified four discrete waves of metastatic expansions, each of which resulted in a number of new, genetically similar metastasis sites that also enriched for particular organs (e.g., abdominal vs bone and brain). The lung played a critical role in facilitating metastatic spread in this patient: the lung was the first site of metastatic escape from the primary breast lesion, subclones at this site were likely the source of all four subsequent metastatic waves, and multiple sites in the lung acted as subclone incubators. Finally, functional annotation revealed that many known drivers or metastasis-promoting tumor mutations in this patient were shared by some, but not all metastatic sites, highlighting the need for more comprehensive surveys of a patient’s metastases for effective clinical intervention. Conclusions Our analysis revealed the presence of substantial tumor evolution at metastatic incubator sites in a patient, with potentially important clinical implications. Our study demonstrated that sampling of a large number of metastatic sites affords unprecedented detail for studying metastatic evolution.

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Single-nucleus RNAseq uncovers multiple tumor lineages in 5 metastatic sites of a prostate adenocarcinoma rapid-autopsy patient

10.26226/morressier.5f69edb69b74b699bf38c66e ◽

2020 ◽

Author(s):

Anna Trigos

Keyword(s):

Prostate Adenocarcinoma ◽

Multiple Tumor ◽

Single Nucleus ◽

Rapid Autopsy ◽

Metastatic Sites

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Novel temporal and spatial patterns of metastatic colonization from rapid-autopsy tumor biopsies

10.1101/2021.06.03.446803 ◽

2021 ◽

Author(s):

Xiaomeng Huang ◽

Yi Qiao ◽

Samuel W Brady ◽

Rachel E Factor ◽

Erinn Downs-Kelly ◽

...

Keyword(s):

Breast Cancer ◽

Metastatic Breast Cancer ◽

Critical Role ◽

Metastatic Breast ◽

Clinical Intervention ◽

Metastatic Spread ◽

Tumor Evolution ◽

Metastatic Colonization ◽

Rapid Autopsy ◽

Metastatic Sites

Background: Metastatic breast cancer is a deadly disease with a low 5-year survival rate. Tracking metastatic spread in living patients is difficult, and thus poorly understood. Results: Via rapid autopsy, we have collected 30 tumor samples over 3 timepoints and across 8 organs from a triple-negative metastatic breast cancer patient. The large number of sites sampled, together with deep whole genome sequencing and advanced computational analysis, allowed us to comprehensively reconstruct the tumor's evolution at subclonal resolution. The most unique, previously not reported aspect of the tumor's evolution we observed in this patient was the presence of "subclone incubators", i.e. already metastatic sites where substantial tumor evolution occurred before colonization of additional sites and organs by subclones that evolved at the incubator site. Overall, we identified four discrete waves of metastatic expansions, each of which resulted in a number of new, genetically similar metastasis sites that also enriched for particular organs (e.g. abdominal vs bone and brain). The lung played a critical role in facilitating metastatic spread in this patient: the lung was the first site of metastatic escape from the primary breast lesion; subclones at this site were the source of all four subsequent metastatic waves; and multiple sites in the lung acted as subclone incubators. Finally, functional annotation revealed that many known driver or metastasis-promoting tumor mutations in this patient were shared by some, but not all metastatic sites, highlighting the need for more comprehensive surveys of a patient's metastases for effective clinical intervention. Conclusions: Our analysis revealed the presence of substantial tumor evolution at metastatic incubator sites, with potentially important clinical implications. Our study demonstrated that sampling of a large number of metastatic sites affords unprecedented detail for studying metastatic evolution.

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Distinct genomic hallmarks exist between metastatic upper and lower tract urothelial carcinoma.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.7_suppl.371 ◽

2019 ◽

Vol 37 (7_suppl) ◽

pp. 371-371

Author(s):

Brian Winters ◽

Navonil De Sarkar ◽

Sonali Arora ◽

Hamid Bolouri ◽

Funda Vakar-Lopez, MD ◽

...

Keyword(s):

Copy Number ◽

Metastatic Tumor ◽

Single Nucleotide Variants ◽

Chromosomal Structure ◽

Whole Exome ◽

Genomic Landscape ◽

Gene Definition ◽

Rapid Autopsy ◽

Metastatic Sites ◽

Genome Scale

371 Background: Although the genomic landscape of LTUC is well studied, less is known about UTUC, including in the metastatic sites. We compared genomic features of metastatic UTUC and LTUC. Methods: We performed whole exome sequencing on 7 rapid autopsy patients with metastatic UC, with matched primary and metastatic tumor samples (N = 37). Single nucleotide variants (SNV) were identified using Mutect and Strelka. Focused analyses were performed on mutations with known significance in UC as well as mutations predicted to have functional impact using 11 mutation assessors. Genome scale copy number aberrations (CNA) were estimated using Sequenza (normalized for ploidy) to derive gene definition restricted copy number estimation outcomes. Multi-dimensional scaling (MDS) was used to visualize how copy number and mutation-derived genomic distances differed between LTUC and UTUC. Results: Three pts with UTUC (3 primary samples, 13 metastases) and four pts with LTUC (4 primary samples, 17 metastases) were examined. The majority of patients were male (5) and received cisplatin-based therapy (5). We found that SNV burden (mean mutation per megabase) was significantly higher in LTUC vs. UTUC overall (6.6 vs. 3.8, p = 0.001) and when stratified by primaries (6.1 vs. 2.9, p = 0.047); or metastases (6.7 vs. 4.1, p = 0.001). Mutational signature analysis revealed higher proportion of APOBEC signature in all LTUC vs. UTUC tumors. Both inter- and intra-individual genomic distances between primary and metastatic tissues were substantially larger in UTUC than LTUC, suggesting a wider spectrum of mutations at the level of individual nucleotides and chromosomal structure. Interestingly, Gene definition-restricted CNA analysis revealed MDM2 amplification exclusively in UTUC tumors which was associated with shallow p53 deletion. Conclusions: Metastatic UTUC appears to have a lower overall mutational burden but greater genomic variability compared to LTUC. Our relatively small dataset suggests that metastatic UTUC displays a greater spectrum of mutational divergence from LTUC which may partially explain differences in clinical behavior.

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Growing indication for FNA to study and analyze tumor heterogeneity at metastatic sites

Cancer Cytopathology ◽

10.1002/cncy.21395 ◽

2014 ◽

Vol 122 (7) ◽

pp. 504-511 ◽

Cited By ~ 16

Author(s):

Francisco Beca ◽

Fernando Schmitt

Keyword(s):

Tumor Heterogeneity ◽

Metastatic Sites

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Fibroblasts from metastatic sites induce broad-spectrum drug desensitization via modulation of mitochondrial priming

10.1101/197376 ◽

2017 ◽

Author(s):

Benjamin D. Landry ◽

Thomas Leete ◽

Ryan Richards ◽

Peter Cruz-Gordillo ◽

Gary Ren ◽

...

Keyword(s):

Stromal Cells ◽

Tumor Heterogeneity ◽

Large Scale ◽

Drug Response ◽

Drug Sensitivity ◽

Tumor Stroma ◽

Fibroblast Cells ◽

Drug Induced ◽

Disease Stratification ◽

Metastatic Sites

ABSTRACTDue to tumor heterogeneity, most believe that effective treatments should be tailored to the features of an individual tumor or tumor subclass. It is still unclear what information should be considered for optimal disease stratification, and most prior work focuses on tumor genomics. Here, we focus on the tumor micro-environment. Using a large-scale co-culture assay optimized to measure drug-induced cell death, we identify tumor-stroma interactions that modulate drug sensitivity. Our data show that the chemo-insensitivity typically associated with aggressive subtypes of breast cancer is not cell intrinsic, but rather a product of tumor-fibroblast interactions. Additionally, we find that fibroblast cells influence tumor drug response in two distinct and divergent manners, which were predicable based on the anatomical origin from which the fibroblasts were harvested. These divergent phenotypes result from modulation of “mitochondrial priming” of tumor cells, caused by secretion of inflammatory cytokines, such as IL6 and IL8, from stromal cells.

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