Autophagy and Cancer
Autophagy is a tightly controlled process of self-digestion with both oncogenic and tumor suppressing activities in cancer1. It is also an important therapeutic target. Key initiating events in autophagy are the lipidation and insertion of the microtubule associated protein 1 light chains A, B and C (MAP1LC3A, B, C, referred to as LC3A, LC3B, and LC3C) into the autophagosomal membrane. There is growing evidence that the LC3 paralogs have different functions in cancer.
In our published work we investigated the role of autophagy in ccRCC. We discovered that VHL regulates two separate autophagic programs distinguished by the LC3 paralogs LC3B and LC3C. VHL tumor suppressor activity inhibits oncogenic LC3B-dependent autophagy, while inducing tumor suppressing LC3C-mediated autophagy (Mikhaylova et al., Cancer Cell, 2012, Hall et al., Cancer Cell, 2014). Our work focuses now on upstream mechanisms regulating LC3C autophagy and on identification of LC3C cargo.
LC3C is an evolutionary late, hominid-specific gene that has several features distinguishing it from its paralogs. In particular, LC3C gained a highly conserved C-terminal 20 amino acid peptide after glycine126, cleaved in the process of its lipidation. We established that LC3C autophagy is tumor suppressing and acts downstream from VHL, the tumor suppressor lost nearly universally in ccRCC. Upon VHL loss, HIF transcriptionally represses LC3C autophagy (Mikhaylova et al., Cancer cell, 2012). Next, we investigated mechanisms of LC3C selective autophagy. LC3C autophagy requires noncanonical regulatory initiation complexes, including ULK3, UVRAG, RUBCN, PIK3C2A, and a member of ESCRT, TSG101. We established that Postdivision Midbody Rings (PDMBs) implicated in cancer stem cell regulation are direct targets of LC3C autophagy. LC3C C-terminal peptide is necessary and sufficient to mediate LC3C-dependent selective degradation of PDMBs (Bischoff et al., 2021). This work establishes a new noncanonical human-specific selective autophagic program relevant to cancer stem cells.
Identification of ccRCC subtypes for development of personalized medicine treatments
Personalized medicine represents a therapeutic advance where specific treatments are established and delivered based on the molecular landscape for an individual patient or a closely defined group of patients. The concept is based on the fact that patients have unique genomic and functional signatures, in addition to the common genomic aberrations. However, identification of clinically effective signature has encountered several limitations. Tumors show complex genetic heterogeneity, both among the same cancers from different patients and within regions of individual tumors. This genetic complexity is further amplified when data from transcriptomics, proteomics, and metabolomics are considered, all affected by various environmental factors. The usefulness of molecular signatures for prediction of treatments in human cancers is hindered by the fact that these signatures are defined by the steady-state levels of measured molecules and do not adequately represent metabolic fluxes and pathway activities. There can also be poor correlation between abundances of molecules identified from different omics. The next generation of system biology approaches will be the integration of data from more than one omics platform. Single-cell omics are likely to improve the efficacy of prognostic and predictive classifiers.
Recently we performed integrated multiomic analysis in order to understand the role of tobacco smoking in ccRCC. Tobacco smoking (TS) is an established risk factor for ccRCC with unknown effects on tumor pathobiology.
We investigated the landscape of ccRCCs and paired normal kidney tissues (NKTs) using integrated transcriptomic, metabolomic and metallomic approaches in a cohort of never smokers (NS) and long-term current smokers (LTS) Caucasian males.
All three Omics domains consistently identified a distinct metabolic subtype of ccRCCs in LTS, characterized by activation of oxidative phosphorylation (OxPhos) coupled with reprogramming of the malate-aspartate shuttle and metabolism of aspartate, glutamate, glutamine and histidine. Cadmium, copper and inorganic arsenic accumulated in LTS tumors showing redistribution among intracellular pools, including relocation of copper into the cytochrome c oxidase complex. Gene expression signature based on the LTS metabolic subtype provided prognostic stratification of The Cancer Genome Atlas (TCGA) ccRCC tumors that was independent from genomic alterations.
The work identified the TS related metabolic subtype of ccRCC with vulnerabilities that can be exploited for precision medicine approaches targeting metabolic pathways. The results provided rationale for the development of metabolic biomarkers with diagnostic and prognostic applications using evaluation of OxPhos status. The metallomic analysis revealed the role of disrupted metal homeostasis in ccRCC highlighting the importance of studying effects of metals from e-cigarettes and environmental exposures.
Current work further investigates transcriptomic signatures and metalomic approaches as prognostic and predictive classifiers.