Differences in Signaling Patterns on PI3K Inhibition Reveal Context Specificity in KRAS-Mutant Cancers

Secretome of Cancer-Associated Fibroblasts (CAFs) Influences Drug Sensitivity in Cancer Cells

Resistance is a major problem with effective cancer treatment and the stroma forms a significant portion of the tumor mass but traditional drug screens involve cancer cells alone. Cancer-associated fibroblasts (CAFs) are a major tumor stroma component and its secreted proteins may influence the function of cancer cells. The majority of secretome studies compare different cancer or CAF cell lines exclusively. Here, we present the direct characterization of the secreted protein profiles between CAFs and KRAS mutant-cancer cell lines from colorectal, lung, and pancreatic tissues using multiplexed mass spectrometry. 2573 secreted proteins were annotated, and differential analysis highlighted understudied CAF-enriched secreted proteins, including Wnt family member 5B (WNT5B), in addition to established CAF markers, such as collagens. The functional role of CAF secreted proteins was explored by assessing its effect on the response to 97 anticancer drugs since stromal cells may cause a differing cancer drug response, which may be missed on routine drug screening using cancer cells alone. CAF secreted proteins caused specific effects on each of the cancer cell lines, which highlights the complexity and challenges in cancer treatment and so the importance to consider stromal elements.

Clinical and Preclinical Targeting of Oncogenic Pathways in PDAC: Targeted Therapeutic Approaches for the Deadliest Cancer

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related death worldwide. It is commonly diagnosed in advanced stages and therapeutic interventions are typically constrained to systemic chemotherapy, which yields only modest clinical outcomes. In this review, we examine recent developments in targeted therapy tailored to address distinct molecular pathway alteration required for PDAC. Our review delineates the principal signaling pathways and molecular mechanisms implicated in the initiation and progression of PDAC. Subsequently, we provide an overview of prevailing guidelines, ongoing investigations, and prospective research trajectories related to targeted therapeutic interventions, drawing insights from randomized clinical trials and other pertinent studies. This review focus on a comprehensive examination of preclinical and clinical data substantiating the efficacy of these therapeutic modalities, emphasizing the potential of combinatorial regimens and novel therapies to enhance the quality of life for individuals afflicted with PDAC. Lastly, the review delves into the contemporary application and ongoing research endeavors concerning targeted therapy for PDAC. This synthesis serves to bridge the molecular elucidation of PDAC with its clinical implications, the evolution of innovative therapeutic strategies, and the changing landscape of treatment approaches.

Tissue-specific identification of multi-omics features for pan-cancer drug response prediction

Current statistical models for drug response prediction and biomarker identification fall short in leveraging the shared and unique information from various cancer tissues and multi-omics profiles. We developed mix-lasso model that introduces an additional sample group penalty term to capture tissue-specific effects of features on pan-cancer response prediction. The mix-lasso model takes into account both the similarity between drug responses (i.e., multi-task learning), and the heterogeneity between multi-omics data (multi-modal learning). When applied to large-scale pharmacogenomics dataset from Cancer Therapeutics Response Portal, mix-lasso enabled accurate drug response predictions and identification of tissue-specific predictive features in the presence of various degrees of missing data, drug-drug correlations, and high-dimensional and correlated genomic and molecular features that often hinder the use of statistical approaches in drug response modeling. Compared to tree lasso model, mix-lasso identified a smaller number of tissue-specific features, hence making the model more interpretable and stable for drug discovery applications.

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Pan-cancer cell lines provide a test bench for exploring gene-drug relationships

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Multi-omics data were integrated with pharmacological profiles for joint modeling

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Mix-lasso identifies tissue-specific biomarkers predictive of multi-drug responses

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Mix-lasso provides small number of stable features for drug discovery applications

Individualized Prediction of Drug Response and Rational Combination Therapy in NSCLC Using Artificial Intelligence-Enabled Studies of Acute Phosphoproteomic Changes

We hypothesize that the study of acute protein perturbation in signal transduction by targeted anticancer drugs can predict drug sensitivity of these agents used as single agents and rational combination therapy. We assayed dynamic changes in 52 phosphoproteins caused by an acute exposure (1 hour) to clinically relevant concentrations of seven targeted anticancer drugs in 35 non-small cell lung cancer (NSCLC) cell lines and 16 samples of NSCLC cells isolated from pleural effusions. We studied drug sensitivities across 35 cell lines and synergy of combinations of all drugs in six cell lines (252 combinations). We developed orthogonal machine-learning approaches to predict drug response and rational combination therapy. Our methods predicted the most and least sensitive quartiles of drug sensitivity with an AUC of 0.79 and 0.78, respectively, whereas predictions based on mutations in three genes commonly known to predict response to the drug studied, for example, EGFR, PIK3CA, and KRAS, did not predict sensitivity (AUC of 0.5 across all quartiles). The machine-learning predictions of combinations that were compared with experimentally generated data showed a bias to the highest quartile of Bliss synergy scores (P = 0.0243). We confirmed feasibility of running such assays on 16 patient samples of freshly isolated NSCLC cells from pleural effusions. We have provided proof of concept for novel methods of using acute ex vivo exposure of cancer cells to targeted anticancer drugs to predict response as single agents or combinations. These approaches could complement current approaches using gene mutations/amplifications/rearrangements as biomarkers and demonstrate the utility of proteomics data to inform treatment selection in the clinic.

Cell line models for drug discovery in PIK3CA-mutated colorectal cancers

Colorectal cancer remains a major cause of cancer-related morbidity and mortality. Metastatic disease is still incurable in most cases. New therapies based on a better understanding of the pathogenesis are needed to improve outcomes. Mutations in the catalytic sub-unit of kinase PI3K encoded by gene PIK3CA are common in colorectal cancer cell lines and patient samples. The characteristics of colorectal cancer cell lines from the Cancer Cell Line Encyclopedia (CCLE), with and without PIK3CA mutations, were evaluated and compared. A panel of colorectal cancer cell lines with and without PIK3CA mutations were compared for their sensitivity to PIK3 inhibitors. Concomitant molecular abnormalities of sensitive versus resistant cell lines were identified. Colorectal cancer cell lines with PIK3CA mutations are commonly diploid and have microsatellite instability (MSI) and a high tumor mutation burden (TMB), compared with cell lines without PIK3CA mutations. Cell lines with PIK3CA mutations tend to have higher sensitivity to some but not all PI3K inhibitors tested and display variability in sensitivity. Both cell lines with MSI and microsatellite stable (MSS) are among the most sensitive to PI3K inhibitors. Multiple concomitant mutations in the PI3K/AKT and KRAS/BRAF/MEK/ERK pathways are often observed in sensitive cell lines. In concordance with patient samples, colorectal cancer cell lines with PIK3CA mutations display more commonly MSI and tend to be more sensitive to PI3K inhibitors. Variability in sensitivity of PIK3CA-mutated cell lines suggests that additional molecular abnormalities contribute to sensitivity.

Daily Practice Assessment of KRAS Status in NSCLC Patients: A New Challenge for the Thoracic Pathologist Is Right around the Corner

Simple Summary

RAS mutation is the most frequent oncogenic alteration in human cancers and KRAS is the most frequently mutated, notably in non-small cell lung carcinomas (NSCLC). Various attempts to inhibit KRAS in the past were unsuccessful in these latter tumors. However, recently, several small molecules (AMG510, MRTX849, JNJ-74699157, and LY3499446) have been developed to specifically target KRAS G12C-mutated tumors, which seems promising for patient treatment and should soon be administered in daily practice for non-squamous (NS)-NSCLC. In this context, it will be mandatory to systematically assess the KRAS status in routine clinical practice, at least in advanced NS-NSCLC, leading to new challenges for thoracic oncologists.

Abstract

KRAS mutations are among the most frequent genomic alterations identified in non-squamous non-small cell lung carcinomas (NS-NSCLC), notably in lung adenocarcinomas. In most cases, these mutations are mutually exclusive, with different genomic alterations currently known to be sensitive to therapies targeting EGFR, ALK, BRAF, ROS1, and NTRK. Recently, several promising clinical trials targeting KRAS mutations, particularly for KRAS G12C-mutated NSCLC, have established new hope for better treatment of patients. In parallel, other studies have shown that NSCLC harboring co-mutations in KRAS and STK11 or KEAP1 have demonstrated primary resistance to immune checkpoint inhibitors. Thus, the assessment of the KRAS status in advanced-stage NS-NSCLC has become essential to setting up an optimal therapeutic strategy in these patients. This stimulated the development of new algorithms for the management of NSCLC samples in pathology laboratories and conditioned reorganization of optimal health care of lung cancer patients by the thoracic pathologists. This review addresses the recent data concerning the detection of KRAS mutations in NSCLC and focuses on the new challenges facing pathologists in daily practice for KRAS status assessment.

A phospho-proteomic study of cetuximab resistance in KRAS/NRAS/BRAFV600 wild-type colorectal cancer

PURPOSE

We hypothesised that plasticity in signal transduction may be a mechanism of drug resistance and tested this hypothesis in the setting of cetuximab resistance in patients with KRAS/NRAS/BRAFV600 wild-type colorectal cancer (CRC).

METHODS

A multiplex antibody-based platform was used to study simultaneous changes in signal transduction of 55 phospho-proteins in 12 KRAS/NRAS/BRAFV600 wild-type CRC cell lines (6 cetuximab sensitive versus 6 cetuximab resistant) following 1 and 4 h in vitro cetuximab exposure. We validated our results in CRC patient samples (n = 4) using ex vivo exposure to cetuximab in KRAS/NRAS/BRAFV600 cells that were immunomagnetically separated from the serous effusions of patients with known cetuximab resistance.

RESULTS

Differences in levels of phospho-proteins in cetuximab sensitive and resistant cell lines included reductions in phospho-RPS6 and phospho-PRAS40 in cetuximab sensitive, but not cetuximab resistant cell lines at 1 and 4 h, respectively. In addition, phospho-AKT levels were found to be elevated in 3/4 patient samples following ex vivo incubation with cetuximab for 1 h. We further explored these findings by studying the effects of combinations of cetuximab and two PI3K pathway inhibitors in 3 cetuximab resistant cell lines. The addition of PI3K pathway inhibitors to cetuximab led to a significantly higher reduction in colony formation capacity compared to cetuximab alone.

CONCLUSION

Our findings suggest activation of the PI3K pathway as a mechanism of cetuximab resistance in KRAS/NRAS/BRAFV600 wild-type CRC.

A potential role for metastasis-associated in colon cancer 1 (MACC1) as a pan-cancer prognostic and immunological biomarker

BACKGROUND

Metastasis-Associated in Colon Cancer 1(MACC1) is a validated biomarker for metastasis and is linked to survival. Although extensive experimental evidence indicates an association between MACC1 and diverse cancers, no pan-cancer analyses have yet been performed for this marker, and the role of MACC1 in immunology remains unknown.

MATERIAL AND METHODS

In our study, we performed the analysis of MACC1 expression and its influence on prognosis using multiple databases, including TIMER2, GEPIA2, Kaplan-Meier plotter. MACC1 promoter methylation levels were evaluated using the UALCAN database. Based on the TCGA database, we explored the relationship between MACC1 and tumor mutational burden (TMB), microsatellite instability (MSI), immune checkpoints using the R programming language. We evaluated the association between MACC1 and immune infiltration via TIMER and UALCAN.

RESULTS

Our results revealed that abnormal DNA methylation may be an important cause for the different expression of MACC1 across cancer types. Meanwhile, we explored the potential oncogenic roles of MACC1 and found significant prognostic value. MACC1 may be related to T-cell function and the polarization of tumor-associated macrophages, especially in STAD and LGG. Its expression was associated with immune infiltration and was found to be closely related to immune checkpoint-associated genes, especially CD274 and SIGLEC15, indicating that MACC1 may be a potential immune therapeutic target for several malignancies. Our paper reveals for the first time the relationship between MACC1 and cancer immunology.

CONCLUSIONS

MACC1 might act as a predictor for the immune response in cancer patients, and could also represent a new potential immunotherapeutic target.

Recent Advances of Functional Proteomics in Gastrointestinal Cancers- a Path towards the Identification of Candidate Diagnostic, Prognostic, and Therapeutic Molecular Biomarkers

Gastrointestinal (GI) cancer remains one of the common causes of morbidity and mortality. A high number of cases are diagnosed at an advanced stage, leading to a poor survival rate. This is primarily attributed to the lack of reliable diagnostic biomarkers and limited treatment options. Therefore, more sensitive, specific biomarkers and curative treatments are desirable. Functional proteomics as a research area in the proteomic field aims to elucidate the biological function of unknown proteins and unravel the cellular mechanisms at the molecular level. Phosphoproteomic and glycoproteomic studies have emerged as two efficient functional proteomics approaches used to identify diagnostic biomarkers, therapeutic targets, the molecular basis of disease and mechanisms underlying drug resistance in GI cancers. In this review, we present an overview on how functional proteomics may contribute to the understanding of GI cancers, namely colorectal, gastric, hepatocellular carcinoma and pancreatic cancers. Moreover, we have summarized recent methodological developments in phosphoproteomics and glycoproteomics for GI cancer studies.

Pan-Cancer Analysis of the Mitophagy-Related Protein PINK1 as a Biomarker for the Immunological and Prognostic Role

Introduction

The PINK1 gene encodes a serine/threonine protein kinase that localizes to mitochondria and has usually been considered to protect cells from stress-induced mitochondrial dysfunction. PINK1 mutations have been observed to lead to autosomal recessive Parkinson’s disease. However, the immunological and prognostic roles of PINK1 across cancers remain unclear.

Material and method

In the current study, we used multiple databases, including Oncomine, PrognoScan, Kaplan-Meier Plotter, GEPIA, TIMER, and cBioportal, to investigate the PINK1 expression distribution and its immunological and prognostic role across cancers.

Results and discussion

Bioinformatics data revealed that the mRNA expression of PINK1 was downregulated in most tumors. Although there was a significant prognostic value of PINK1 expression across cancers, PINK1 played a protective or detrimental role in different kinds of cancers. Liver hepatocellular carcinoma and lung squamous cell carcinoma were selected as representative cancer types for further exploration. We found that PINK1 always played a protective role in liver hepatocellular carcinoma patients in the stratified prognostic analyses of clinicopathological characteristics. There were contradictory results between liver hepatocellular carcinoma and lung squamous cell carcinoma in the correlations of PINK1 expression with immune infiltration, including infiltration of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells. Furthermore, specific markers of B cells and CD8+ T cells also exhibited different PINK1-related immune infiltration patterns. In addition, there was a significant association between PINK1 copy number variations and immune infiltrates across cancers.

Conclusion

The mitophagy-related protein PINK1 can work as a biomarker for prognosis and the immune response across cancers.

Nf1-Mutant Tumors Undergo Transcriptome and Kinome Remodeling after Inhibition of either mTOR or MEK

Loss of the tumor suppressor NF1 leads to activation of RAS effector pathways, which are therapeutically targeted by inhibition of mTOR (mTORi) or MEK (MEKi). However, therapeutic inhibition of RAS effectors leads to the development of drug resistance and ultimately disease progression. To investigate molecular signatures in the context of NF1 loss and subsequent acquired drug resistance, we analyzed the exomes, transcriptomes, and kinomes of Nf1-mutant mouse tumor cell lines and derivatives of these lines that acquired resistance to either MEKi or mTORi. Biochemical comparisons of this unique panel of tumor cells, all of which arose in Nf1^+/- mice, indicate that loss of heterozygosity of Nf1 as an initial genetic event does not confer a common biochemical signature or response to kinase inhibition. Although acquired drug resistance by Nf1-mutant tumor cells was accompanied by altered kinomes and irreversibly altered transcriptomes, functionally in multiple Nf1-mutant tumor cell lines, MEKi resistance was a stable phenotype, in contrast to mTORi resistance, which was reversible. Collectively, these findings demonstrate that Nf1-mutant tumors represent a heterogeneous group biochemically and undergo broader remodeling of kinome activity and gene expression in response to targeted kinase inhibition.

Precision Detection Technology: Equipping Precision Oncology with Wings

In recent years, precision medical detection techniques experienced a rapid transformation from low-throughput to high-throughput genomic sequencing, from multicell promiscuous detection to single-cell precision sequencing. The emergence of liquid biopsy technology has compensated for the many limitations of tissue biopsy, leading to a tremendous transformation in precision detection. Precision detection techniques contribute to monitoring disease development more closely, evaluating therapeutic effects more scientifically, and developing new targets and new drugs. In the future, the role of precision detection and the joint detection in epigenetics, rare gene detection, individualized targeted therapy, and multigene targeted drug combination therapy should be extensively explored. This article reviews the changes in precision medical detection technology in the era of precision medicine, as well as the development, clinical application, and future challenges of liquid biopsy.