APOBEC3 as a driver of genetic intratumor heterogeneity

Intratumor heterogeneity is partly responsible for the poor prognosis of glioblastoma (GBM) patients. In this study, we aimed to assess the effect of different heterogeneous subregions of GBM on overall survival (OS) stratification. A total of 105 GBM patients were retrospectively enrolled and divided into long-term and short-term OS groups. Four MRI sequences, including contrast-enhanced T1-weighted imaging (T1C), T1, T2, and FLAIR, were collected for each patient. Then, 4 heterogeneous subregions, i.e. the region of entire abnormality (rEA), the regions of contrast-enhanced tumor (rCET), necrosis (rNec) and edema/non-contrast-enhanced tumor (rE/nCET), were manually drawn from the 4 MRI sequences. For each subregion, 50 radiomics features were extracted. The stratification performance of 4 heterogeneous subregions, as well as the performances of 4 MRI sequences, was evaluated both alone and in combination. Our results showed that rEA was superior in stratifying long-and short-term OS. For the 4 MRI sequences used in this study, the FLAIR sequence demonstrated the best performance of survival stratification based on the manual delineation of heterogeneous subregions. Our results suggest that heterogeneous subregions of GBMs contain different prognostic information, which should be considered when investigating survival stratification in patients with GBM.

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Single Cell Genetic Profiling of Tumors of Breast Cancer Patients Aged 50 Years and Older Reveals Enormous Intratumor Heterogeneity Independent of Individual Prognosis

Cancers ◽

10.3390/cancers13133366 ◽

2021 ◽

Vol 13 (13) ◽

pp. 3366

Author(s):

Anna-Sophie Liegmann ◽

Kerstin Heselmeyer-Haddad ◽

Annette Lischka ◽

Daniela Hirsch ◽

Wei-Dong Chen ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Copy Number ◽

Genome Instability ◽

Single Cells ◽

Gene Mutations ◽

Intratumor Heterogeneity ◽

Breast Cancer Patients ◽

Luminal A ◽

Pik3ca Mutations

Purpose: Older breast cancer patients are underrepresented in cancer research even though the majority (81.4%) of women dying of breast cancer are 55 years and older. Here we study a common phenomenon observed in breast cancer which is a large inter- and intratumor heterogeneity; this poses a tremendous clinical challenge, for example with respect to treatment stratification. To further elucidate genomic instability and tumor heterogeneity in older patients, we analyzed the genetic aberration profiles of 39 breast cancer patients aged 50 years and older (median 67 years) with either short (median 2.4 years) or long survival (median 19 years). The analysis was based on copy number enumeration of eight breast cancer-associated genes using multiplex interphase fluorescence in situ hybridization (miFISH) of single cells, and by targeted next-generation sequencing of 563 cancer-related genes. Results: We detected enormous inter- and intratumor heterogeneity, yet maintenance of common cancer gene mutations and breast cancer specific chromosomal gains and losses. The gain of COX2 was most common (72%), followed by MYC (69%); losses were most prevalent for CDH1 (74%) and TP53 (69%). The degree of intratumor heterogeneity did not correlate with disease outcome. Comparing the miFISH results of diploid with aneuploid tumor samples significant differences were found: aneuploid tumors showed significantly higher average signal numbers, copy number alterations (CNAs) and instability indices. Mutations in PIKC3A were mostly restricted to luminal A tumors. Furthermore, a significant co-occurrence of CNAs of DBC2/MYC, HER2/DBC2 and HER2/TP53 and mutual exclusivity of CNAs of HER2 and PIK3CA mutations and CNAs of CCND1 and PIK3CA mutations were revealed. Conclusion: Our results provide a comprehensive picture of genome instability profiles with a large variety of inter- and intratumor heterogeneity in breast cancer patients aged 50 years and older. In most cases, the distribution of chromosomal aneuploidies was consistent with previous results; however, striking exceptions, such as tumors driven by exclusive loss of chromosomes, were identified.

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Discovery of Spatial Peptide Signatures for Neuroblastoma Risk Assessment by MALDI Mass Spectrometry Imaging

Cancers ◽

10.3390/cancers13133184 ◽

2021 ◽

Vol 13 (13) ◽

pp. 3184

Author(s):

Zhiyang Wu ◽

Patrick Hundsdoerfer ◽

Johannes H. Schulte ◽

Kathy Astrahantseff ◽

Senguel Boral ◽

...

Keyword(s):

Mass Spectrometry ◽

Risk Assessment ◽

Mass Spectrometry Imaging ◽

Principal Component ◽

Therapy Resistance ◽

Risk Classification ◽

Intratumor Heterogeneity ◽

Ionization Mass ◽

Characteristic Analysis ◽

Analytical Strategy

Risk classification plays a crucial role in clinical management and therapy decisions in children with neuroblastoma. Risk assessment is currently based on patient criteria and molecular factors in single tumor biopsies at diagnosis. Growing evidence of extensive neuroblastoma intratumor heterogeneity drives the need for novel diagnostics to assess molecular profiles more comprehensively in spatial resolution to better predict risk for tumor progression and therapy resistance. We present a pilot study investigating the feasibility and potential of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to identify spatial peptide heterogeneity in neuroblastoma tissues of divergent current risk classification: high versus low/intermediate risk. Univariate (receiver operating characteristic analysis) and multivariate (segmentation, principal component analysis) statistical strategies identified spatially discriminative risk-associated MALDI-based peptide signatures. The AHNAK nucleoprotein and collapsin response mediator protein 1 (CRMP1) were identified as proteins associated with these peptide signatures, and their differential expression in the neuroblastomas of divergent risk was immunohistochemically validated. This proof-of-concept study demonstrates that MALDI-MSI combined with univariate and multivariate analysis strategies can identify spatially discriminative risk-associated peptide signatures in neuroblastoma tissues. These results suggest a promising new analytical strategy improving risk classification and providing new biological insights into neuroblastoma intratumor heterogeneity.

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A Systems Approach to Brain Tumor Treatment

Cancers ◽

10.3390/cancers13133152 ◽

2021 ◽

Vol 13 (13) ◽

pp. 3152

Author(s):

James H. Park ◽

Adrian Lopez Garcia de Lomana ◽

Diego M. Marzese ◽

Tiffany Juarez ◽

Abdullah Feroze ◽

...

Keyword(s):

Brain Tumor ◽

Brain Tumors ◽

Rational Design ◽

Clinical Medicine ◽

Systems Approach ◽

Therapy Resistance ◽

High Rate ◽

Intratumor Heterogeneity ◽

Primary Therapy ◽

Molecular Features

Brain tumors are among the most lethal tumors. Glioblastoma, the most frequent primary brain tumor in adults, has a median survival time of approximately 15 months after diagnosis or a five-year survival rate of 10%; the recurrence rate is nearly 90%. Unfortunately, this prognosis has not improved for several decades. The lack of progress in the treatment of brain tumors has been attributed to their high rate of primary therapy resistance. Challenges such as pronounced inter-patient variability, intratumoral heterogeneity, and drug delivery across the blood–brain barrier hinder progress. A comprehensive, multiscale understanding of the disease, from the molecular to the whole tumor level, is needed to address the intratumor heterogeneity resulting from the coexistence of a diversity of neoplastic and non-neoplastic cell types in the tumor tissue. By contrast, inter-patient variability must be addressed by subtyping brain tumors to stratify patients and identify the best-matched drug(s) and therapies for a particular patient or cohort of patients. Accomplishing these diverse tasks will require a new framework, one involving a systems perspective in assessing the immense complexity of brain tumors. This would in turn entail a shift in how clinical medicine interfaces with the rapidly advancing high-throughput (HTP) technologies that have enabled the omics-scale profiling of molecular features of brain tumors from the single-cell to the tissue level. However, several gaps must be closed before such a framework can fulfill the promise of precision and personalized medicine for brain tumors. Ultimately, the goal is to integrate seamlessly multiscale systems analyses of patient tumors and clinical medicine. Accomplishing this goal would facilitate the rational design of therapeutic strategies matched to the characteristics of patients and their tumors. Here, we discuss some of the technologies, methodologies, and computational tools that will facilitate the realization of this vision to practice.

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Structural variant detection in cancer genomes: computational challenges and perspectives for precision oncology

npj Precision Oncology ◽

10.1038/s41698-021-00155-6 ◽

2021 ◽

Vol 5 (1) ◽

Author(s):

Ianthe A. E. M. van Belzen ◽

Alexander Schönhuth ◽

Patrick Kemmeren ◽

Jayne Y. Hehir-Kwa

Keyword(s):

Intratumor Heterogeneity ◽

Precision Oncology ◽

Single Nucleotide Variants ◽

Full Spectrum ◽

Single Nucleotide ◽

Sequencing Technologies ◽

Cancer Genomes ◽

Genomic Aberrations

AbstractCancer is generally characterized by acquired genomic aberrations in a broad spectrum of types and sizes, ranging from single nucleotide variants to structural variants (SVs). At least 30% of cancers have a known pathogenic SV used in diagnosis or treatment stratification. However, research into the role of SVs in cancer has been limited due to difficulties in detection. Biological and computational challenges confound SV detection in cancer samples, including intratumor heterogeneity, polyploidy, and distinguishing tumor-specific SVs from germline and somatic variants present in healthy cells. Classification of tumor-specific SVs is challenging due to inconsistencies in detected breakpoints, derived variant types and biological complexity of some rearrangements. Full-spectrum SV detection with high recall and precision requires integration of multiple algorithms and sequencing technologies to rescue variants that are difficult to resolve through individual methods. Here, we explore current strategies for integrating SV callsets and to enable the use of tumor-specific SVs in precision oncology.

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Frequent PIK3CA mutations in eutopic endometrium of patients with ovarian clear cell carcinoma

Modern Pathology ◽

10.1038/s41379-021-00861-3 ◽

2021 ◽

Author(s):

Kosuke Murakami ◽

Akiko Kanto ◽

Kazuko Sakai ◽

Chiho Miyagawa ◽

Hisamitsu Takaya ◽

...

Keyword(s):

Ovarian Cancer ◽

Cell Carcinoma ◽

Clear Cell ◽

Clear Cell Carcinoma ◽

Driver Mutations ◽

Intratumor Heterogeneity ◽

Ovarian Clear Cell Carcinoma ◽

Eutopic Endometrium ◽

Stromal Component ◽

Hotspot Mutations

AbstractRecent studies have reported cancer-associated mutations in normal endometrium. Mutations in eutopic endometrium may lead to endometriosis and endometriosis-associated ovarian cancer. We investigated PIK3CA mutations (PIK3CAm) for three hotspots (E542K, E545K, H1047R) in eutopic endometrium in patients with ovarian cancer and endometriosis from formalin-fixed paraffin-embedded specimens by laser-capture microdissection and droplet digital PCR. The presence of PIK3CAm in eutopic endometrial glands with mutant allele frequency ≥ 15% were as follows: ovarian clear cell carcinoma (OCCC) with PIK3CAm in tumors, 20/300 hotspots in 11/14 cases; OCCC without PIK3CAm, 42/78 hotspots in 11/12 cases; high-grade serous ovarian carcinoma, 8/45 hotspots in 3/5 cases; and endometriotic cysts, 5/63 hotspots in 5/6 cases. These rates were more frequent than in noncancer nonendometriosis controls (7/309 hotspots in 5/17 cases). In OCCC without PIK3CAm, 7/12 (58%) cases showed multiple hotspot mutations in the same eutopic endometrial glands. In 3/54 (5.6%) cases, PIK3CAm was found in eutopic endometrial stroma. Multisampling of the OCCC tumors with PIK3CAm showed intratumor heterogeneity in three of eight cases. In two cases, PIK3CAm was detected in the stromal component of the tumor. Homogenous PIK3CAm in the epithelial component of the tumor matched the mutation in eutopic endometrial glands in only one case. Eutopic endometrial glands in ovarian cancer and endometriosis show high frequency of PIK3CAm that is not consistent with tumors, and multiple hotspot mutations are often found in the same glands. While the mutations identified in eutopic endometrium may not be driver mutations in the patient’s cancer, these are still driver mutations but this specific clone has not undergone the requisite steps for the development of cancer.

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