ABSTRACT: Cancer-cell intrinsic gene expression signatures minimise the confounding effects of intratumoural heterogeneity in colorectal cancer patient classification.
Project description:Assessing the impact of genomic alterations on protein networks is fundamental in identifying the mechanisms that shape cancer heterogeneity. We have used isobaric labelling to characterize the proteomic landscapes of 50 colorectal cancer cell lines and to decipher the functional consequences of somatic genomic variants. The robust quantification of over 9,000 proteins and 11,000 phosphopeptides on average, enabled the de novo construction of a functional protein correlation network which ultimately exposed the collateral effects of mutations on protein complexes. CRISPR-cas9 deletion of key chromatin modifiers confirmed that the consequences of genomic alterations can propagate through protein interactions in a transcript-independent manner. Lastly, we leveraged the quantified proteome to perform unsupervised classification of the cell lines and to build predictive models of drug response in colorectal cancer. Overall, we provide a deep integrative view of the functional network and the molecular structure underlying the heterogeneity of colorectal cancer cells.
Project description:The heterogeneity and multiple histological categories of soft tissue sarcoma (STS) underlie a need for better classification schemes to improve their management. As none are currently available, we aimed to derive hypoxia and intrinsic molecular subtype mRNA abundance signatures for localized soft tissue sarcoma. RNA sequencing was used to identify genes induced by hypoxia in seven STS cell lines.
Project description:Assessing the impact of genomic alterations on protein networks is fundamental in identifying the mechanisms that shape cancer heterogeneity. We have used isobaric labelling to characterize the proteomic landscapes of 50 colorectal cancer cell lines and to decipher the functional consequences of somatic genomic variants. The robust quantification of over 9,000 proteins and 11,000 phosphopeptides on average, enabled the de novo construction of a functional protein correlation network which ultimately exposed the collateral effects of mutations on protein complexes. CRISPR-cas9 deletion of key chromatin modifiers confirmed that the consequences of genomic alterations can propagate through protein interactions in a transcript-independent manner. Lastly, we leveraged the quantified proteome to perform unsupervised classification of the cell lines and to build predictive models of drug response in colorectal cancer. Overall, we provide a deep integrative view of the functional network and the molecular structure underlying the heterogeneity of colorectal cancer cells.
Project description:Triple-Negative Breast Cancer (TNBC) is a heterogeneous collection of cancers where personalized treatment is difficult and chemotherapy and immunotherapy combinations are the main treatment options. Many attempts to tackle patient heterogeneity have focused on defining cancer-intrinsic subtypes based on differential tumor mRNA-expression across patient cohorts. While these multi-gene diagnostics have shown success in hormone receptor- positive cancers (e.g. OncotypeDX), no TNBC classifiers have shown clinical utility in predicting patient survival or treatment response. We hypothesize that TNBC-infiltrating immune-cells both affect mRNA-based classification and contribute to treatment response variability. To evaluate this hypothesis, we benchmarked the performance of common TNBC-classification (TNBC-type) and infiltrating immune (CIBERSORT) algorithms on the same underlying datasets. Encouragingly, we found that – as with OncotypeDx– highly proliferative TNBC-subtypes (BL1) show the strongest evidence of response to cytotoxic chemotherapies. Interestingly, this cancer-proliferative signature (BL1) is strongly correlated with enrichment in tumor-infiltrating lymphocyte signatures (TIL) which show superior prognostic and predictive power. In addition, Tumor Associated Macrophage (TAM) signatures show independent predictive and prognostic power for both patient survival and response to anthracycline- and taxane-based chemotherapies. These gene signature-based correlations were validated in a new independent cohort of 67 TNBC-patients treated with neoadjuvant chemotherapy. Overall, these results argue for the independent contributions of both cancer-intrinsic and -extrinsic factors in predicting treatment response in the neoadjuvant setting.
Project description:Immuno-genomic classification of colorectal cancer organoids reveals cancer cells with intrinsic immunogenic properties associated with patient survival
Project description:Recent work built on published transcriptional subtypes of colorectal cancer (CRC) has shown that tumor stromal content impacts CRC classification, with both clinical and biological implications. To Tackle the issue of intrinsic epithelial gene profiles, we assembled a dataset of matched liver metastatic colorectal cancer and Patients Derived Xenografts. in which the stroma of the original tumor is substituted by murine tissue, we deployed a molecularly annotated collection of 515 PDXs from 244 patients as a resource for species (human)-specific assessment of cancer-cell intrinsic transcriptional features. We identified five ColoRectal cancer Intrinsic Subtypes (CRIS) endowed with functional and phenotypic characteristics related to specific genetic traits, most of which were not evidenced in the transcriptional subgroups reported in previous studies.
Project description:Recent work built on published transcriptional subtypes of colorectal cancer (CRC) has shown that tumor stromal content impacts CRC classification, with both clinical and biological implications. To Tackle the issue of intrinsic epithelial gene profiles, we assembled a dataset of matched liver metastatic colorectal cancer and Patients Derived Xenografts. in which the stroma of the original tumor is substituted by murine tissue, we deployed a molecularly annotated collection of 515 PDXs from 244 patients as a resource for species (human)-specific assessment of cancer-cell intrinsic transcriptional features. We identified five ColoRectal cancer Intrinsic Subtypes (CRIS) endowed with functional and phenotypic characteristics related to specific genetic traits, most of which were not evidenced in the transcriptional subgroups reported in previous studies.
Project description:Tumor heterogeneity derives from diverse populations of cancer cells that contribute unique properties to the microenvironment and tumor progression. While multiple classification systems have been developed to define tumor subtypes in colorectal cancer (CRC), the lack of single cell resolution has prohibited a better understanding of how these distinct cancer cell subtypes influence the overall tumor phenotype. Here were report that two cancer stem cell subtypes that are oncogenic versions of the intestinal Crypt Base Columnar (CBC) and Regenerative Stem Cell (RSC) populations, are commonly co-present in human CRC. We develop subtype-specific xenograft models utilizing a heterogeneous patient cell line (SW480) that contains CBCs and RSCs, and we define their tumor microenvironments at high resolution via single cell RNA sequencing. RSCs create differentiated, inflammatory, slow growing tumors. CBCs create proliferative, immune-suppressive, invasive tumors. With this enhanced resolution, we unify current CRC patient classification schema with TME phenotypes and organization.
Project description:Tumor heterogeneity derives from diverse populations of cancer cells that contribute unique properties to the microenvironment and tumor progression. While multiple classification systems have been developed to define tumor subtypes in colorectal cancer (CRC), the lack of single cell resolution has prohibited a better understanding of how these distinct cancer cell subtypes influence the overall tumor phenotype. Here were report that two cancer stem cell subtypes that are oncogenic versions of the intestinal Crypt Base Columnar (CBC) and Regenerative Stem Cell (RSC) populations, are commonly co-present in human CRC. We develop subtype-specific xenograft models utilizing a heterogeneous patient cell line (SW480) that contains CBCs and RSCs, and we define their tumor microenvironments at high resolution via single cell RNA sequencing. RSCs create differentiated, inflammatory, slow growing tumors. CBCs create proliferative, immune-suppressive, invasive tumors. With this enhanced resolution, we unify current CRC patient classification schema with TME phenotypes and organization.
