The Functional Effects of Key Driver KRAS Mutations on Gene Expression in Lung Cancer

Aging directs the differential evolution of KRAS-driven lung adenocarcinoma

Lung adenocarcinoma (LUAD), the most common histological subtype of lung cancer( 1, 2 ), is a disease of the elderly, with an average age of diagnosis of about 70 years of age( 3 ). Older age is associated with an increased incidence of KRAS-driven LUAD( 4 ), a particularly deadly type of LUAD characterized by treatment resistance and relapse. Despite this, our understanding of how old age shapes KRAS-driven LUAD evolution remains incomplete. While the age-related increase in cancer risk was previously ascribed to the accumulation of mutations over time, we are now beginning to consider the role of host biology as an independent factor influencing cancer. Here, we use single-cell RNA-Sequencing of KP (Kras G12D/+ ; Trp53 flox/flox ) LUAD transplanted into young and old mice to define how old age affects LUAD evolution and map the changes that old age imposes onto LUAD's microenvironment. Our data demonstrates that the aged lung environment steers LUAD evolution towards a primitive stem-like state that is associated with poor prognosis. We ascribe this differential evolution, at least in part, to a population of rare and highly secretory damage-associated alveolar differentiation intermediate (ADI) cells that accumulate in the aged tumor microenvironment (TME) and that dominate the niche signaling received by LUAD cells. Overall, our data puts aging center stage in coordinating LUAD evolution, highlighting the need to model LUAD in its most common context and creating a framework to tailor future cancer therapeutic strategies to the age of the patient to improve outcomes in the largest and most vulnerable LUAD patient population, the elderly.

Integrating Clinical Cancer and PTM Proteomics Data Identifies a Mechanism of ACK1 Kinase Activation

Beyond the most common oncogenes activated by mutation (mut-drivers), there likely exists a variety of low-frequency mut-drivers, each of which is a possible frontier for targeted therapy. To identify new and understudied mut-drivers, we developed a machine learning (ML) model that integrates curated clinical cancer data and posttranslational modification (PTM) proteomics databases. We applied the approach to 62,746 patient cancers spanning 84 cancer types and predicted 3,964 oncogenic mutations across 1,148 genes, many of which disrupt PTMs of known and unknown function. The list of putative mut-drivers includes established drivers and others with poorly understood roles in cancer. This ML model is available as a web application. As a case study, we focused the approach on nonreceptor tyrosine kinases (NRTK) and found a recurrent mutation in activated CDC42 kinase-1 (ACK1) that disrupts the Mig6 homology region (MHR) and ubiquitin-association (UBA) domains on the ACK1 C-terminus. By studying these domains in cultured cells, we found that disruption of the MHR domain helps activate the kinase while disruption of the UBA increases kinase stability by blocking its lysosomal degradation. This ACK1 mutation is analogous to lymphoma-associated mutations in its sister kinase, TNK1, which also disrupt a C-terminal inhibitory motif and UBA domain. This study establishes a mut-driver discovery tool for the research community and identifies a mechanism of ACK1 hyperactivation shared among ACK family kinases.

IMPLICATIONS

This research identifies a potentially targetable activating mutation in ACK1 and other possible oncogenic mutations, including PTM-disrupting mutations, for further study.

Assessment of spatial transcriptomics for oncology discovery

Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for oncology discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis, which provides a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell-type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.

Patients' and professionals' views related to ethical issues in precision medicine: a mixed research synthesis

BACKGROUND

Precision medicine development is driven by the possibilities of next generation sequencing, information technology and artificial intelligence and thus, raises a number of ethical questions. Empirical studies have investigated such issues from the perspectives of health care professionals, researchers and patients. We synthesize the results from these studies in this review.

METHODS

We used a systematic strategy to search, screen and assess the literature for eligibility related to our research question. The initial search for empirical studies in five data bases provided 665 different records and we selected 92 of these publications for inclusion in this review. Data were extracted in a spreadsheet and categorized into different topics representing the views on ethical issues in precision medicine.

RESULTS

Many patients and professionals expect high benefits from precision medicine and have a positive attitude towards it. However, patients and professionals also perceive some risks. Commonly perceived risks include: lack of evidence for accuracy of tests and efficacy of treatments; limited knowledge of patients, which makes informed consent more difficult; possible unavailability of access to precision medicine for underprivileged people and ethnic minorities; misuse of data by insurance companies and employers, potential of racial stigmatization due to genetic information; unwanted communication of incidental findings; changes in doctor-patient-relationship through focusing on data; and the problem that patients could feel under pressure to optimize their health.

CONCLUSIONS

National legislation and guidelines already minimize many risks associated with precision medicine. However, from our perspective some problems require more attention. Should hopes for precision medicine's benefits be fulfilled, then the ethical principle of justice would require an unlimited access to precision medicine for all people. The potential for autonomous patients' decisions must be greatly enhanced by improvements in patient education. Harm from test results must be avoided in any case by the highest possible data security level and communication guidelines. Changes in the doctor-patient relationship and the impact of precision medicine on the quality of life should be further investigated. Additionally, the cost-effectiveness of precision medicine should be further examined, in order to avoid malinvestment.