Andrew M Hudson

Cancer-Associated Protein Kinase C Mutations Reveal Kinase’s Role as Tumor Suppressor

Protein kinase C (PKC) isozymes have remained elusive cancer targets despite the unambiguous tumor promoting function of their potent ligands, phorbol esters, and the prevalence of their mutations. We analyzed 8% of PKC mutations identified in human cancers and found that, surprisingly, most were loss of function and none were activating. Loss-of-function mutations occurred in all PKC subgroups and impeded second-messenger binding, phosphorylation, or catalysis. Correction of a loss-of-function PKCβ mutation by CRISPR-mediated genome editing in a patient-derived colon cancer cell line suppressed anchorage-independent growth and reduced tumor growth in a xenograft model. Hemizygous deletion promoted anchorage-independent growth, revealing that PKCβ is haploinsufficient for tumor suppression. Several mutations were dominant negative, suppressing global PKC signaling output, and bioinformatic analysis suggested that PKC mutations cooperate with co-occurring mutations in cancer drivers. These data establish that PKC isozymes generally function as tumor suppressors, indicating that therapies should focus on restoring, not inhibiting, PKC activity.

Mixed lineage kinases activate MEK independently of RAF to mediate resistance to RAF inhibitors

RAF inhibitor therapy yields significant reductions in tumour burden in the majority of V600E-positive melanoma patients; however, resistance occurs within 2–18 months. Here we demonstrate that the mixed lineage kinases (MLK1–4) are MEK kinases that reactivate the MEK/ERK pathway in the presence of RAF inhibitors. Expression of MLK1–4 mediates resistance to RAF inhibitors and promotes survival in V600E-positive melanoma cell lines. Furthermore, we observe upregulation of the MLKs in 9 of 21 melanoma patients with acquired drug resistance. Consistent with this observation, MLKs promote resistance to RAF inhibitors in mouse models and contribute to acquired resistance in a cell line model. Lastly, we observe that a majority of MLK1 mutations identified in patients are gain-of-function mutations. In summary, our data demonstrate a role for MLKs as direct activators of the MEK/ERK pathway with implications for melanomagenesis and resistance to RAF inhibitors.

Discrepancies in Cancer Genomic Sequencing Highlight Opportunities for Driver Mutation Discovery

Cancer genome sequencing is being used at an increasing rate to identify actionable driver mutations that can inform therapeutic intervention strategies. A comparison of two of the most prominent cancer genome sequencing databases from different institutes (Cancer Cell Line Encyclopedia and Catalogue of Somatic Mutations in Cancer) revealed marked discrepancies in the detection of missense mutations in identical cell lines (57.38% conformity). The main reason for this discrepancy is inadequate sequencing of GC-rich areas of the exome. We have therefore mapped over 400 regions of consistent inadequate sequencing (cold-spots) in known cancer-causing genes and kinases, in 368 of which neither institute finds mutations. We demonstrate, using a newly identified PAK4 mutation as proof of principle, that specific targeting and sequencing of these GC-rich cold-spot regions can lead to the identification of novel driver mutations in known tumor suppressors and oncogenes. We highlight that cross-referencing between genomic databases is required to comprehensively assess genomic alterations in commonly used cell lines and that there are still significant opportunities to identify novel drivers of tumorigenesis in poorly sequenced areas of the exome. Finally, we assess other reasons for the observed discrepancy, such as variations in dbSNP filtering and the acquisition/loss of mutations, to give explanations as to why there is a discrepancy in pharmacogenomic studies, given recent concerns with poor reproducibility of data.

Somatically mutated ABL1 is an actionable and essential NSCLC survival gene

The lack of actionable mutations in patients with non‐small cell lung cancer (NSCLC) presents a significant hurdle in the design of targeted therapies for this disease. Here, we identify somatically mutated ABL1 as a genetic dependency that is required to maintain NSCLC cell survival. We demonstrate that NSCLC cells with ABL1 mutations are sensitive to ABL inhibitors and we verify that the drug‐induced effects on cell viability are specific to pharmacological inhibition of the ABL1 kinase. Furthermore, we confirm that imatinib suppresses lung tumor growth in vivo, specifically in lung cancer cells harboring a gain‐of‐function (GOF) mutation in ABL1. Consistent with structural modeling, we demonstrate that mutations in ABL1 identified in primary NSCLC tumors and a lung cancer cell line increase downstream pathway activation compared to wild‐type ABL1. Finally, we observe that the ABL1 cancer mutants display an increased cytosolic localization, which is associated with the oncogenic properties of the ABL1 kinase. In summary, our results suggest that NSCLC patients with ABL1 mutations could be stratified for treatment with imatinib in combination with other therapies.

Using large-scale genomics data to identify driver mutations in lung cancer: methods and challenges

Lung cancer is the commonest cause of cancer death in the world and carries a poor prognosis for most patients. While precision targeting of mutated proteins has given some successes for never- and light-smoking patients, there are no proven targeted therapies for the majority of smokers with the disease. Despite sequencing hundreds of lung cancers, known driver mutations are lacking for a majority of tumors. Distinguishing driver mutations from inconsequential passenger mutations in a given lung tumor is extremely challenging due to the high mutational burden of smoking-related cancers. Here we discuss the methods employed to identify driver mutations from these large datasets. We examine different approaches based on bioinformatics, in silico structural modeling and biological dependency screens and discuss the limitations of these approaches.

Abstract 125: Protein kinase C loss-of-function mutations in cancer reveal role as tumor suppressor

Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA Protein kinase C (PKC) remains an elusive chemotherapeutic target despite decades of research. To determine whether PKC isozymes function as oncogenes or tumor suppressors, we analyzed 8% of PKC mutations identified in human cancers. Surprisingly, the majority were loss-of-function and none were activating. Loss-of-function mutations were found in all PKC subgroups and acted by impeding 2nd messenger binding or preventing processing phosphorylations. Bioinformatic analysis revealed that PKC mutations might cooperate with co-occurring mutations in cancer drivers. Correction of a patient-identified, loss-of-function PKCβ mutation by CRISPR-mediated genome editing, in a colon cancer cell line, suppressed anchorage-independent growth and reduced tumor growth in xenograft models. Hemizygous deletion provided an anchorage-independent growth advantage, revealing PKC is haploinsufficient for tumor suppression. Several mutations were dominant-negative, suppressing global PKC signaling output. These data establish that PKC isozymes generally function as tumor suppressors, indicating that therapeutic strategies should focus on restoring PKC activity, not inhibiting it. Citation Format: Corina E. Antal, Andrew M. Hudson, Emily Kang, Ciro Zanca, Christopher Wirth, Natalie L. Stephenson, Eleanor W. Trotter, Lisa L. Gallegos, Crispin Miller, Frank Furnari, Tony Hunter, John Brognard, Alexandra C. Newton. Protein kinase C loss-of-function mutations in cancer reveal role as tumor suppressor. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 125. doi:10.1158/1538-7445.AM2015-125

Truncation and Motif Based Pan‐Cancer Analysis Highlights Novel Tumor Suppressing Kinases.

A major challenge in cancer genomics is identifying driver mutations from the large number of neutral passenger mutations within a given tumor. Here, we utilize motifs critical for kinase activity to functionally filter genomic data to identify driver mutations that would otherwise be lost within mutational noise. In the first step of our screen, we define a putative tumor suppressing kinome by identifying kinases with truncation mutations occurring within or before the kinase domain. We aligned these kinase sequences and, utilizing data from the Cancer Cell Line Encyclopedia and The Cancer Genome Atlas databases, identified amino acids that represent predicted hotspots for loss-of-function mutations. The functional consequences of new LOF mutations were validated and the top 15 hotspot LOF residues were used in a pan-cancer analysis to define the tumor-suppressing kinome. A ranked list revealed MAP2K7 as a candidate tumor suppressor in gastric cancer, despite the mutational frequency of MAP2K7 falling within the mutational noise for this cancer type. The majority of mutations in MAP2K7 abolished catalytic activity compared to the wild type kinase, consistent with a tumor suppressive role for MAP2K7 in gastric cancer. Furthermore, reactivation of the JNK pathway in gastric cancer cells harboring LOF mutations in MAP2K7 or JNK1 suppresses clonogenicity and growth in soft agar, demonstrating the functional importance of inactivating the JNK pathway in gastric cancer. In summary, our data highlights a broadly applicable strategy to identify functional cancer driver mutations leading us to define the JNK pathway as tumor suppressive in gastric cancer. Summary A unique computational pan-cancer analysis pinpoints novel tumor suppressing kinases, and highlights the power of functional genomics by defining the JNK pathway as tumor suppressive in gastric cancer.

Truncation- and motif-based pan-cancer analysis reveals tumor-suppressing kinases

A kinase domain–focused functional genomic screen reveals loss-of-function mutations in a JNK pathway kinase in gastric cancer. Sorting through the noise Genomic sequencing has been a boon to understanding, diagnosing, and treating cancer and other diseases, but it can be difficult to sort the “driver” mutations from natural variants and silent mutations, particularly in such heterogeneous samples as tumors. Hudson et al. used a combination of bioinformatics, structural modeling, and biochemistry to identify loss-of-function, driver mutations in kinases that as yet have been lost in the noise of sequencing data in the TCGA and CCLE databases. By focusing their analysis on the sequences surrounding the generally conserved catalytic domain, the authors identified a broadly tumor-suppressive kinome, which revealed critical loss-of-function mutations in the kinase MAP2K7 in stomach cancers. Restoring mutant gastric cancer cells with a functional kinase reduced their growth in culture models, indicating an avenue to explore further for clinical benefit. A major challenge in cancer genomics is identifying “driver” mutations from the many neutral “passenger” mutations within a given tumor. To identify driver mutations that would otherwise be lost within mutational noise, we filtered genomic data by motifs that are critical for kinase activity. In the first step of our screen, we used data from the Cancer Cell Line Encyclopedia and The Cancer Genome Atlas to identify kinases with truncation mutations occurring within or before the kinase domain. The top 30 tumor-suppressing kinases were aligned, and hotspots for loss-of-function (LOF) mutations were identified on the basis of amino acid conservation and mutational frequency. The functional consequences of new LOF mutations were biochemically validated, and the top 15 hotspot LOF residues were used in a pan-cancer analysis to define the tumor-suppressing kinome. A ranked list revealed MAP2K7, an essential mediator of the c-Jun N-terminal kinase (JNK) pathway, as a candidate tumor suppressor in gastric cancer, despite its mutational frequency falling within the mutational noise for this cancer type. The majority of mutations in MAP2K7 abolished its catalytic activity, and reactivation of the JNK pathway in gastric cancer cells harboring LOF mutations in MAP2K7 or the downstream kinase JNK suppressed clonogenicity and growth in soft agar, demonstrating the functional relevance of inactivating the JNK pathway in gastric cancer. Together, our data highlight a broadly applicable strategy to identify functional cancer driver mutations and define the JNK pathway as tumor-suppressive in gastric cancer.

Abstract 4408: Mutant ABL1 is a genetic dependency in non-small cell lung cancer amenable to pharmacological intervention

Non-small cell lung cancer (NSCLC) is the most frequent lung cancer subtype and it affects near 1.5 million patients worldwide annually. The lack of actionable mutations in NSCLC patients presents a significant hurdle in the administration of targeted therapies for this disease, and their identification is an urgent unmet need. Here we identify somatically mutated ABL1 as a genetic dependency that is required to maintain NSCLC cell survival. We demonstrate that NSCLC cancer cells with ABL1 mutations are sensitive to ABL inhibitors and we verify that the drug-induced effects on cell viability are specific to pharmacological inhibition of the ABL1 kinase. Consistent with structural modeling, we demonstrate that mutations in ABL1 identified in primary NSCLC tumors and a lung cancer cell line increase downstream pathway activation compared to wild-type ABL1. Finally, we show that imatinib suppresses lung tumor growth in vivo, specifically in lung cancer cells harboring a gain-of-function (GOF) mutation in ABL1. In summary, our results suggest that NSCLC patients with ABL1 mutations could be stratified for treatment with imatinib in combination with other therapies. Citation Format: PEDRO Torres-Ayuso, Ewelina Testoni, Natalie L. Stephenson, Anna A. Marusiak, Eleanor W. Trotter, Andrew Hudson, Cassandra L. Hodgkinson, Christopher J. Morrow, Caroline Dive, John Brognard. Mutant ABL1 is a genetic dependency in non-small cell lung cancer amenable to pharmacological intervention. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4408.

Abstract A2-18: The challenges of using large-scale genomics data to identify novel drivers of lung cancer

Lung cancer is one of the major causes of cancer deaths worldwide and only 30% of patients survive the disease for at least one year after diagnosis. Patients are often too frail to receive systemic chemotherapy and there is an urgent need for less toxic, efficacious, targeted therapies. Despite recent efforts with large-scale genomics data we still lack knowledge about driver mutations for the majority of lung cancers. Increasingly, cancer researchers are using online cancer genomic databases to identify novel targets to investigate. A comparison of two prominent databases from different institutes (CCLE and COSMIC) revealed marked discrepancies in the detection of missense mutations in identical cell lines (57.38% conformity). A major reason for this discrepancy is inadequate sequencing of GC-rich areas. This is a significant issue for lung cancer, with a mutation signature predominantly affecting guanine and cytosine nucleotides and therefore preferring GC-rich regions. We have therefore focused on GC-rich regions that next-generation-sequencing struggle to cover and discovered over 400 of these regions (cold-spots) in Cancer Consensus and kinase genes alone. We demonstrate how a PAK4 mutation, found in a GC-rich cold-spot in a lung adenocarcinoma cell line, activates the pERK pathway. This suggests that specific targeting of GC-rich regions may be required to uncover further oncogenes and tumour suppressors in lung cancer. The high mutational burden of lung cancer creates additional challenges in distinguishing driver mutations from a multitude of passenger mutations. One solution is to use siRNA knockdown screens on all genes that are mutated in a cell line and assess cell viability. However we demonstrate that inconsistencies in mutational profiling of cell lines and passaging effects have the potential to influence these types of studies. These limitations also offer new explanations for the discrepancies seen when comparing pharmacogenomics studies. Citation Format: Andrew M. Hudson, Tim Yates, Chris Wirth, Yaoyong Li, Wendy Trotter, Shameem Fawdar, Crispin Miller, John Brognard. The challenges of using large-scale genomics data to identify novel drivers of lung cancer. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-18.