Biology subjects

Karlic, R.

Publications and source records attributed to Karlic, R..

5 recordsLinked to original sources

bioRxiv2025

Lymphangiogenesis driven by VEGF-C reshapes tumor immune landscape and enables tumor eradication by viral immunotherapy

Vascular Endothelial Growth Factor C (VEGF-C) is a key lymphangiogenic growth factor expressed by many types of cancer. Here, we investigated the effects of tumor-derived VEGF-C on the therapeutic efficacy of oncolytic virotherapy with avian paramyxoviruses APMV-1 (NDV) and AMPV-4. Treatment of B16F10 tumors not expressing VEGF-C with either virus led to tumor growth delay, and complete responses were rare. In contrast, when tumors expressed VEGF-C, viral therapy led to complete remission and long-term immunological memory in most mice. Upon re-challenge, most mice remained tumor- and metastasis-free for over 18 months, indicating durable immunity. VEGF-C induced tumor lymphangiogenesis, which correlated with high CD8+ and CD4+ T-cell densities in proximity of lymphatic vessels. Spectral flow cytometry revealed distinct changes in the composition and activation of CD8+, CD4+ T cells and NK cells associated with complete remission. In responders, tumors were highly enriched in CD8+CD25+ PD-1+ effector T cells. Composition of T cells was altered in sentinel and in contralateral lymph nodes, indicating a systemic immune response. Taken together, these data demonstrate VEGF-C-induced changes in tumor immune landscape which are critical for achieving complete response and support combining VEGF-C with APMV virotherapy as a novel highly effective strategy for cancer treatment.

cancer biology↗

bioRxiv2025

Learning the cellular origins of cancer using single-cell chromatin landscapes

Deciphering the cell of origin (COO) of different cancers is critical for understanding tumor development and improving diagnostic and therapeutic strategies in oncology. Previous studies demonstrated that the COO chromatin accessibility landscape shapes the genomic distribution of cancer somatic mutations. We leveraged machine learning, 559 single-cell chromatin accessibility cellular profiles, and 2,734 whole genome sequencing patient samples to predict the COO of 36 cancer subtypes with high robustness and accuracy, confirming both the known anatomical and cellular origins of numerous human cancers, often at cell subset resolution. Importantly, our data-driven approach predicts that basal cells give rise to small cell lung cancers, challenging the traditional view of neuroendocrine COO. Our study also highlights distinct cellular trajectories during cancer development of different histological subtypes and uncovers an intermediate metaplastic state during tumorigenesis for multiple gastrointestinal cancers, which have important implications for cancer prevention, early detection, and treatment stratification.

cancer biology↗

bioRxiv2019

Tumor mutational landscape is a record of the pre-malignant state

Chromatin structure has a major influence on the cell-specific density of somatic mutations along the cancer genome. Here, we present a pan-cancer study in which we searched for the putative cancer cell-of-origin of 2,550 whole genomes, representing 32 cancer types by matching their mutational landscape to the regional patterns of chromatin modifications ascertained in 104 normal tissue types. We found that, in almost all cancer types, the cell-of-origin can be predicted solely from their DNA sequences. Our analysis validated the hypothesis that high-grade serous ovarian cancer originates in the fallopian tube and identified distinct origins of breast cancer subtypes. We also demonstrated that the technique is equally capable of identifying the cell-of-origin for a series of 2,044 metastatic samples from 22 of the tumor types available as primaries. Moreover, cancer drivers, whether inherited or acquired, reside in active chromatin regions in the respective cell-of-origin. Taken together, our findings highlight that many somatic mutations accumulate while the chromatin structure of the cell-of-origin is maintained and that this historical record, captured in the DNA, can be used to identify the often elusive cancer cell-of-origin.

genomics↗

bioRxiv2019

Somatic mutation landscape reveals differential variability of cell-of-origin for primary liver cancer

BackgroundPrimary liver tissue cancers display consistent increase in global disease burden and mortality. Identification of cell-of-origins for primary liver cancers would be a necessity to expand options for designing relevant therapeutics and preventive medicine for these cancer types. Previous reports on cell-of-origin for primary liver cancers was mainly from animal studies, and integrative research utilizing human specimen data was poorly established.\n\nMethodsWe analyzed a whole-genome sequencing data set for a total of 363 tumor and progenitor tissues along with 423 normal tissue epigenomic marks to predict the cell-of-origin for primary liver cancer subtypes.\n\nResultsDespite the mixed histological features, the predicted cell-of-origin for mixed hepatocellular carcinoma / intrahepatic cholangiocarcinoma were uniformly predicted as a hepatocytic origin. Individual sample-level prediction revealed differential level of cell-of-origin heterogeneity depending on the primary liver cancer types, with more heterogeneity observed in intrahepatic cholangiocarcinomas. Additional analyses on the whole genome sequencing data of hepatic progenitor cells suggest these progenitor cells might not a direct cell-of-origin for liver cancers.\n\nConclusionsThese results provide novel insights on the heterogeneous nature and potential contributors of cell-of-origin predictions for primary liver cancers.

genomics↗

bioRxiv2017

Accurate Discrimination of 23 Major Cancer Types via Whole Genome Somatic Mutation Patterns

The two strongest factors predicting a human cancers clinical behaviour are the primary tumours anatomic organ of origin and its histopathology. However, roughly 3% of the time a cancer presents with metastatic disease and no primary can be determined even after a thorough radiological survey. A related dilemma arises when a radiologically defined mass is sampled by cytology yielding cancerous cells, but the cytologist cannot distinguish between a primary tumour and a metastasis from elsewhere.\n\nHere we use whole genome sequencing (WGS) data from the ICGC/TCGA PanCancer Analysis of Whole Genomes (PCAWG) project to develop a machine learning classifier able to accurately distinguish among 23 major cancer types using information derived from somatic mutations alone. This demonstrates the feasibility of automated cancer type discrimination based on next-generation sequencing of clinical samples. In addition, this work opens the possibility of determining the origin of tumours detected by the emerging technology of deep sequencing of circulating cell-free DNA in blood plasma.

cancer biology↗