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Between-tumor and within-tumor heterogeneity in invasive potential.

ABSTRACT: For women with access to healthcare and early detection, breast cancer deaths are caused primarily by metastasis rather than growth of the primary tumor. Metastasis has been difficult to study because it happens deep in the body, occurs over years, and involves a small fraction of cells from the primary tumor. Furthermore, within-tumor heterogeneity relevant to metastasis can also lead to therapy failures and is obscured by studies of bulk tissue. Here we exploit heterogeneity to identify molecular mechanisms of metastasis. We use "organoids", groups of hundreds of tumor cells taken from a patient and grown in the lab, to probe tumor heterogeneity, with potentially thousands of organoids generated from a single tumor. We show that organoids have the character of biological replicates: within-tumor and between-tumor variation are of similar magnitude. We develop new methods based on population genetics and variance components models to build between-tumor and within-tumor statistical tests, using organoids analogously to large sibships and vastly amplifying the test power. We show great efficiency for tests based on the organoids with the most extreme phenotypes and potential cost savings from pooled tests of the extreme tails, with organoids generated from hundreds of tumors having power predicted to be similar to bulk tests of hundreds of thousands of tumors. We apply these methods to an association test for molecular correlates of invasion, using a novel quantitative invasion phenotype calculated as the spectral power of the organoid boundary. These new approaches combine to show a strong association between invasion and protein expression of Keratin 14, a known biomarker for poor prognosis, with p = 2 × 10-45 for within-tumor tests of individual organoids and p < 10-6 for pooled tests of extreme tails. Future studies using these methods could lead to discoveries of new classes of cancer targets and development of corresponding therapeutics. All data and methods are available under an open source license at https://github.com/baderzone/invasion_2019.

SUBMITTER: Padmanaban V

PROVIDER: S-EPMC6994152 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Between-tumor and within-tumor heterogeneity in invasive potential.

Padmanaban Veena V Tsehay Yohannes Y Cheung Kevin J KJ Ewald Andrew J AJ Bader Joel S JS

PLoS computational biology 20200121 1

For women with access to healthcare and early detection, breast cancer deaths are caused primarily by metastasis rather than growth of the primary tumor. Metastasis has been difficult to study because it happens deep in the body, occurs over years, and involves a small fraction of cells from the primary tumor. Furthermore, within-tumor heterogeneity relevant to metastasis can also lead to therapy failures and is obscured by studies of bulk tissue. Here we exploit heterogeneity to identify molecu ...[more]

PMID: 31961880

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Project description:BACKGROUND: There is currently little support to understand which pathological factors led to differences in tumor texture as measured from FDG PET/CT images. We studied whether tumor heterogeneity measured using texture analysis in FDG-PET/CT images is correlated with pathological prognostic factors in invasive breast cancer. METHODS: Fifty-four patients with locally advanced breast cancer who had an initial FDG-PET/CT were retrospectively included. In addition to SUVmax, three robust textural indices extracted from 3D matrices: High-Gray-level Run Emphasis (HGRE), Entropy and Homogeneity were studied. Univariate and multivariate logistic regression was used to identify PET parameters associated with poor prognosis pathological factors: hormone receptor negativity, presence of HER-2 and triple negative phenotype. Receiver operating characteristic (ROC) curves and the (AUC) analysis, and reclassification measures, were performed in order to evaluate the performance of combining texture analysis and SUVmax for characterizing breast tumors. RESULTS: Tumor heterogeneity, measured with HGRE, was higher in negative estrogen receptor (p?=?0.039) and negative progesterone receptor tumors (p?=?0.036), and in Scarff-Bloom-Richardson grade 3 tumors (p?=?0.047). None of the PET indices could identify HER-2 positive tumors. Only SUVmax was positively correlated with Ki-67 (p<0.0004). Triple negative breast cancer (TNBC) exhibited higher SUVmax (Odd Ratio?=?1.22, 95%CI [1.06-1.39],p?=?0.004), lower Homogeneity (OR?=?3.57[0.98-12.5],p?=?0.05) and higher HGRE (OR?=?8.06[1.88-34.51],p?=?0.005) than non-TNBC. Multivariate analysis showed that HGRE remained associated with TNBC (OR?=?5.27[1.12-1.38],p?=?0.03) after adjustment for SUVmax. Combining SUVmax and HGRE yielded in higher area under the ROC curves (AUC) than SUVmax for identifying TNBC: AUC?=? 0.83 and 0.77, respectively. Probability of correct classification also increased in 77% (10/13) of TNBC and 71% (29/41) of non-TNBC (p?=?0.003), when combining SUVmax and HGRE. CONCLUSIONS: Tumor heterogeneity measured on FDG-PET/CT was higher in invasive breast cancer with poor prognosis pathological factors. Texture analysis might be used, in addition to SUVmax, as a new tool to assess invasive breast cancer aggressiveness.

Project description:ImportanceIntratumoral heterogeneity has been recognized as a significant barrier in successfully developing targetable biomarkers for gastroesophageal adenocarcinoma (GEA) and may affect neoadjuvant precision medicine approaches.ObjectiveTo describe intratumoral spatial heterogeneity of tumor cell populations in nonmetastatic GEA and its association with survival.Design, setting, and participantsThis case series retrospectively identified 41 patients with GEA who underwent up-front surgical resection at a tertiary referral cancer center from January 1, 1989, through December 31, 2013. Survival was calculated from date of surgery to date of death through June 1, 2017. Data were analyzed from June 2, 2017, to March 1, 2019.Main outcomes and measuresOverall survival, intratumoral clonal composition determined by genomic single-nucleotide variation array and bioinformatic analysis, and intercellular tumoral distances determined by multiprobe fluorescence in situ hybridization.ResultsAmong the 41 patients included in the analysis (22 men [54%]; mean [SD] age, 63 [12] years), a high proportion (19 [46%]) presented with tumors possessing high intratumoral heterogeneity. Kaplan-Meier analysis demonstrated that cases with an intratumoral clonal composition count of at least 2 exhibited worse survival compared with cases with a clonal composition count of 0 to 1 (univariate hazard ratio, 3.92; 95% CI, 1.27-12.08; P = .02). This finding remained significant on multivariate analysis controlling for stage, Lauren histologic subtype, receipt of adjuvant therapy, and age (multivariate hazard ratio, 4.55; 95% CI, 1.09-19.04; P = .04). Multiprobe fluorescence in situ hybridization demonstrated intratumoral clonal populations coexisting at submillimeter distances with differing relevant oncogenic copy number alterations, such as EGFR, JAK2, FGFR2, MET, CCND1, KRAS, MYC, PIK3CA, CD274, and PDCD1LG2.Conclusions and relevanceThis study found that spatial intratumoral heterogeneity of oncogenic copy number alterations exists before metastatic dissemination, and increased heterogeneity was associated with worse outcomes in resected GEA. Baseline heterogeneity illustrates the challenges in GEA targeted therapy. Further study may offer insight into strategies on combinatorial and/or sequential targeted and immunotherapeutic approaches.

Project description:Background:Cellular heterogeneity within the tumor microenvironment is essential to tumorigenesis and tumor development. A high-resolution global view of the tumor-infiltrating immune and stromal cells in breast tumors is needed. Methods:xCell was used to create a cellular heterogeneity map of 64 cell types in 1,092 breast tumor and adjacent normal tissues. xCell digitally dissects tissue cellular heterogeneity based on gene expression. Integrated statistical analyses were then performed. Results:There were noticeable differences between the cell fractions in tumor tissues and normal tissues. Tumors displayed higher proportions of immune cells, including CD4+ Tem, CD8+ naïve T cells, and CD8+ Tcm compared with normal tissues. Immune inhibitory receptors (PD1, CTLA4, LAG3 and TIM3) were co-expressed on certain subtypes of T cells in breast tumors, and PD1 and CTLA4 were both positively correlated with CD8+ Tcm and CD8+ T cells. 28 cell types were significantly associated with overall survival in univariate analysis. CD4+ Tem, CD8+ Tcm, CD8+ T-cells, CD8+ naive T-cells, and B cells were positive prognostic factors but CD4+ naive T-cells were negative prognostic factors for breast cancer patients. TDRD6 and TTK are promising T cell and B cell targets for tumor vaccines. Endothelial cells and fibroblasts were significantly less prevalent in tumor tissues; astrocytes and mesangial cells were negatively correlated with the T stage. Mesangial cells and keratinocytes were found to be favorable prognostic factors and myocytes were negative prognostic factors. Five cell types were found to be independent prognostic factors and we used these to create a reliable prognostic model for breast cancer patients. Cellular heterogeneity was discovered among different breast cancer subtypes by Her2, ER, and PR status. Tri-negative patients had the highest fraction of immune cells while luminal type patients had the lowest. The various cells may have diverse or opposing roles in the prognosis of breast cancer patients. Conclusions:We created a uniquecellular map for the diverse heterogeneity of immune and stromal phenotypes within the breast tumor microenvironment. This map may lead to potential therapeutic targets and biomarkers with prognostic utility.

Project description:BackgroundIntra-tumor heterogeneity (ITH) of colorectal cancer (CRC) complicates molecular tumor classification, such as transcriptional subtyping. Differences in cellular states, biopsy cell composition, and tumor microenvironment may all lead to ITH. Here we analyze ITH at the transcriptomic and proteomic levels to ascertain whether subtype discordance between multiregional biopsies reflects relevant biological ITH or lack of classifier robustness. Further, we study the impact of tumor location on ITH.MethodsMultiregional biopsies from stage II and III CRC tumors were analyzed by RNA sequencing (41 biopsies, 14 tumors) and multiplex immune protein analysis (89 biopsies, 29 tumors). CRC subtyping was performed using consensus molecular subtypes (CMS), CRC intrinsic subtypes (CRIS), and TUMOR types. ITH-scores and network maps were defined to determine the origin of heterogeneity. A validation cohort was used with one biopsy per tumor (162 tumors).ResultsOverall, inter-tumor transcriptional variation exceeded ITH, and subtyping calls were frequently concordant between multiregional biopsies. Still, some tumors had high transcriptional ITH and were classified discordantly. Subtyping of proximal MSS tumors were discordant for 50% of the tumors, this ITH was related to differences in the microenvironment. Subtyping of distal MSS tumors were less discordant, here the ITH was more cancer-cell related. The subtype discordancy reflected actual molecular ITH within the tumors. The relevance of the subtypes was reflected at protein level where several inflammation markers were significantly increased in immune related transcriptional subtypes, which was verified in an independent cohort (Wilcoxon rank sum test; p<0.05). Unsupervised hierarchical clustering of the protein data identified large ITH at protein level; as the multiregional biopsies clustered together for only 9 out of 29 tumors.ConclusionOur transcriptomic and proteomic analyses show that the tumor location along the colorectum influence the ITH of CRC, which again influence the concordance of subtyping.

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Between-tumor and within-tumor heterogeneity in invasive potential.

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Between-tumor and within-tumor heterogeneity in invasive potential.

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