Proteogenomic analysis of chemo-refractory high-grade serous ovarian cancer.

To improve the understanding of chemo-refractory high-grade serous ovarian cancers (HGSOCs), we characterized the proteogenomic landscape of 242 (refractory and sensitive) HGSOCs, representing one discovery and two validation cohorts across two biospecimen types (formalin-fixed paraffin-embedded and frozen). We identified a 64-protein signature that predicts with high specificity a subset of HGSOCs refractory to initial platinum-based therapy and is validated in two independent patient cohorts. We detected significant association between lack of Ch17 loss of heterozygosity (LOH) and chemo-refractoriness. Based on pathway protein expression, we identified 5 clusters of HGSOC, which validated across two independent patient cohorts and patient-derived xenograft (PDX) models. These clusters may represent different mechanisms of refractoriness and implicate putative therapeutic vulnerabilities.

Proteogenomic data and resources for pan-cancer analysis.

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigates tumors from a proteogenomic perspective, creating rich multi-omics datasets connecting genomic aberrations to cancer phenotypes. To facilitate pan-cancer investigations, we have generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to create a cohesive and powerful dataset for scientific discovery. We outline efforts by the CPTAC pan-cancer working group in data harmonization, data dissemination, and computational resources for aiding biological discoveries. We also discuss challenges for multi-omics data integration and analysis, specifically the unique challenges of working with both nucleotide sequencing and mass spectrometry proteomics data.

iProMix: A mixture model for studying the function of ACE2 based on bulk proteogenomic data.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused over six million deaths in the ongoing COVID-19 pandemic. SARS-CoV-2 uses ACE2 protein to enter human cells, raising a pressing need to characterize proteins/pathways interacted with ACE2. Large-scale proteomic profiling technology is not mature at single-cell resolution to examine the protein activities in disease-relevant cell types. We propose iProMix, a novel statistical framework to identify epithelial-cell specific associations between ACE2 and other proteins/pathways with bulk proteomic data. iProMix decomposes the data and models cell-type-specific conditional joint distribution of proteins through a mixture model. It improves cell-type composition estimation from prior input, and utilizes a non-parametric inference framework to account for uncertainty of cell-type proportion estimates in hypothesis test. Simulations demonstrate iProMix has well-controlled false discovery rates and favorable powers in non-asymptotic settings. We apply iProMix to the proteomic data of 110 (tumor adjacent) normal lung tissue samples from the Clinical Proteomic Tumor Analysis Consortium lung adenocarcinoma study, and identify interferon α/γ response pathways as the most significant pathways associated with ACE2 protein abundances in epithelial cells. Strikingly, the association direction is sex-specific. This result casts light on the sex difference of COVID-19 incidences and outcomes, and motivates sex-specific evaluation for interferon therapies.

MiXcan: a framework for cell-type-aware transcriptome-wide association studies with an application to breast cancer.

Human bulk tissue samples comprise multiple cell types with diverse roles in disease etiology. Conventional transcriptome-wide association study approaches predict genetically regulated gene expression at the tissue level, without considering cell-type heterogeneity, and test associations of predicted tissue-level expression with disease. Here we develop MiXcan, a cell-type-aware transcriptome-wide association study approach that predicts cell-type-level expression, identifies disease-associated genes via combination of cell-type-level association signals for multiple cell types, and provides insight into the disease-critical cell type. As a proof of concept, we conducted cell-type-aware analyses of breast cancer in 58,648 women and identified 12 transcriptome-wide significant genes using MiXcan compared with only eight genes using conventional approaches. Importantly, MiXcan identified genes with distinct associations in mammary epithelial versus stromal cells, including three new breast cancer susceptibility genes. These findings demonstrate that cell-type-aware transcriptome-wide analyses can reveal new insights into the genetic and cellular etiology of breast cancer and other diseases.

A Flexible Approach for Assessing Heterogeneity of Causal Treatment Effects on Patient Survival Using Large Datasets with Clustered Observations.

Personalized medicine requires an understanding of treatment effect heterogeneity. Evolving toward causal evidence for scenarios not studied in randomized trials necessitates a methodology using real-world evidence. Herein, we demonstrate a methodology that generates causal effects, assesses the heterogeneity of the effects and adjusts for the clustered nature of the data. This study uses a state-of-the-art machine learning survival model, riAFT-BART, to draw causal inferences about individual survival treatment effects, while accounting for the variability in institutional effects; further, it proposes a data-driven approach to agnostically (as opposed to a priori hypotheses) ascertain which subgroups exhibit an enhanced treatment effect from which intervention, relative to global evidence-average treatment effects measured at the population level. Comprehensive simulations show the advantages of the proposed method in terms of bias, efficiency and precision in estimating heterogeneous causal effects. The empirically validated method was then used to analyze the National Cancer Database.

A flexible approach for causal inference with multiple treatments and clustered survival outcomes.

When drawing causal inferences about the effects of multiple treatments on clustered survival outcomes using observational data, we need to address implications of the multilevel data structure, multiple treatments, censoring, and unmeasured confounding for causal analyses. Few off-the-shelf causal inference tools are available to simultaneously tackle these issues. We develop a flexible random-intercept accelerated failure time model, in which we use Bayesian additive regression trees to capture arbitrarily complex relationships between censored survival times and pre-treatment covariates and use the random intercepts to capture cluster-specific main effects. We develop an efficient Markov chain Monte Carlo algorithm to draw posterior inferences about the population survival effects of multiple treatments and examine the variability in cluster-level effects. We further propose an interpretable sensitivity analysis approach to evaluate the sensitivity of drawn causal inferences about treatment effect to the potential magnitude of departure from the causal assumption of no unmeasured confounding. Expansive simulations empirically validate and demonstrate good practical operating characteristics of our proposed methods. Applying the proposed methods to a dataset on older high-risk localized prostate cancer patients drawn from the National Cancer Database, we evaluate the comparative effects of three treatment approaches on patient survival, and assess the ramifications of potential unmeasured confounding. The methods developed in this work are readily available in the R $$ \mathsf{R}\kern.15em $$ package riAFTBART $$ \mathsf{riAFTBART} $$ .

Immunotherapy Outcomes in Individuals With Non-Small Cell Lung Cancer and Poor Performance Status.

BACKGROUND: Patients with non-small cell lung cancer (NSCLC) treated in real-world practice typically have worse performance status (PS) compared with clinical trial patients, and the effectiveness of immunotherapy in this population in unknown. In this study, we assessed the effectiveness of standard of care immunotherapy for the first-line treatment of stage IV patients with NSCLC with Eastern Cooperative Oncology Group (ECOG) PS greater than or equal to 2. METHODS: We selected ECOG PS greater than or equal to 2 patients from real-world oncology data from a deidentified database and included them if they were diagnosed with stage IV NSCLC and had documented Programmed death-ligand 1 [PD-(L)1] expression greater than 0. Patients with tumor PD-(L)1 expression of at least 50% treated with pembrolizumab monotherapy were compared with those who did not have any documented treatment. Patients with tumor PD-(L)1 expression less than 50% treated with pembrolizumab and chemotherapy were compared with those treated with pembrolizumab monotherapy and those without documented treatment. RESULTS: In our propensity score-adjusted analysis, patients with ECOG PS of at least 2 and tumor PD-(L)1 expression of at least 50% treated with pembrolizumab monotherapy had statistically significantly better real-world overall survival compared with those without documented treatment (adjusted hazard ratio [HR] = 0.39, 95% confidence internal [CI] = 0.32 to 0.47). For patients with tumor PD-(L)1 expression less than 50%, there was also a statistically significant real-world overall survival benefit for those who received treatment either with combination pembrolizumab plus chemotherapy (adjusted HR = 0.39, 95% CI = 0.32 to 0.46) or pembrolizumab monotherapy (adjusted HR = 0.55, 95% CI = 0.41 to 0.70) compared with patients receiving no documented treatment. CONCLUSIONS: Among a highly representative sample of patients with advanced NSCLC and poor PS, our findings suggest that immunotherapy may provide an important survival benefit in individuals with high PD-(L)1-expressing tumors and in conjunction with chemotherapy in tumors with low PD-(L)1 expression.

NR2F1 Is a Barrier to Dissemination of Early-Stage Breast Cancer Cells.

Cancer cells can disseminate during very early and sometimes asymptomatic stages of tumor progression. Though biological barriers to tumorigenesis have been identified and characterized, the mechanisms that limit early dissemination remain largely unknown. We report here that the orphan nuclear receptor nuclear receptor subfamily 2, group F, member 1 (NR2F1)/COUP-TF1 serves as a barrier to early dissemination. NR2F1 expression was decreased in patient ductal carcinoma in situ (DCIS) samples. High-resolution intravital imaging of HER2+ early-stage cancer cells revealed that loss of function of NR2F1 increased in vivo dissemination and was accompanied by decreased E-cadherin expression, activation of wingless-type MMTV integration site family, member 1 (WNT)-dependent ß-catenin signaling, disorganized laminin 5 deposition, and increased expression of epithelial-mesenchymal transition (EMT) genes such as twist basic helix-loop-helix transcription factor 1 (TWIST1), zinc finger E-box binding homeobox 1 (ZEB1), and paired related homeobox 1 (PRRX1). Furthermore, downregulation of NR2F1 promoted a hybrid luminal/basal phenotype. NR2F1 expression was positively regulated by p38α signaling and repressed by HER2 and WNT4 pathways. Finally, early cancer cells with NR2F1LOW/PRRX1HIGH staining were observed in DCIS samples. Together, these findings reveal the existence of an inhibitory mechanism of dissemination regulated by NR2F1 in early-stage breast cancer cells. SIGNIFICANCE: During early stages of breast cancer progression, HER2-mediated suppression of NR2F1 promotes dissemination by inducing EMT and a hybrid luminal/basal-like program.

Prospective Cohort Study to Investigate the Safety of Preoperative Tumor Necrosis Factor Inhibitor Exposure in Patients With Inflammatory Bowel Disease Undergoing Intra-abdominal Surgery.

BACKGROUND & AIMS: Whether preoperative treatment of inflammatory bowel disease (IBD) with tumor necrosis factor inhibitors (TNFis) increases the risk of postoperative infectious complications remains controversial. The primary aim of this study was to determine whether preoperative exposure to TNFis is an independent risk factor for postoperative infectious complications within 30 days of surgery. METHODS: We conducted a multicenter prospective observational study of patients with IBD undergoing intra-abdominal surgery across 17 sites from the Crohn's & Colitis Foundation Clinical Research Alliance. Infectious complications were categorized as surgical site infections (SSIs) or non-SSIs. Current TNFi exposure was defined as use within 12 weeks of surgery, and serum was collected for drug-level analyses. Multivariable models for occurrence of the primary outcome, any infection, or SSI were adjusted by predefined covariates (age, sex, preoperative steroid use, and disease type), baseline variables significantly associated (P < .05) with any infection or SSI separately, and TNFi exposure status. Exploratory models used TNFi exposure based on serum drug concentration. RESULTS: A total of 947 patients were enrolled from September 2014 through June 2017. Current TNFi exposure was reported by 382 patients. Any infection (18.1% vs 20.2%, P = .469) and SSI (12.0% vs 12.6%, P = .889) rates were similar in patients currently exposed to TNFis and those unexposed. In multivariable analysis, current TNFi exposure was not associated with any infection (odds ratio, 1.050; 95% confidence interval, 0.716-1.535) or SSI (odds ratio, 1.249; 95% confidence interval, 0.793-1.960). Detectable TNFi drug concentration was not associated with any infection or SSI. CONCLUSIONS: Preoperative TNFi exposure was not associated with postoperative infectious complications in a large prospective multicenter cohort.

A Role for CXCR3 Ligands as Biomarkers of Post-Operative Crohn's Disease Recurrence.

BACKGROUND AND AIMS: Crohn's disease [CD] recurrence following ileocolic resection [ICR] is common. We sought to identify blood-based biomarkers associated with CD recurrence. METHODS: CD patients undergoing ICR were recruited across six centres. Serum samples were obtained at post-operative colonoscopy. A multiplex immunoassay was used to analyse 92 inflammation-related proteins [Olink Proteomics]. Bayesian analysis was used to identify proteins associated with increasing Rutgeerts score. Identified proteins were used in receiver operating characteristic [ROC] analysis to examine the ability to identify CD recurrence [Rutgeerts score ≥i2]. Existing single cell data were interrogated to further elucidate the role of the identified proteins. RESULTS: Data from 276 colonoscopies in 213 patients were available. Median time from surgery to first and second colonoscopy was 7 (interquartile range [IQR] 6-9) and 19 [IQR 16-23] months, respectively. Disease recurrence was evident at 60 [30%] first and 36 [49%] second colonoscopies. Of 14 proteins significantly associated with Rutgeerts score, the strongest signal was seen for CXCL9 and MMP1. Among patients on anti-tumour necrosis factor drugs, CXCL9 and CXCL11 were most strongly associated with Rutgeerts score. Both are CXCR3 ligands. Incorporation of identified proteins into ROC analysis improved the ability to identify disease recurrence as compared to C-reactive protein alone: area under the curve [AUC] 0.75 (95% confidence interval [CI]: 0.66-0.82] vs 0.64 [95% CI 0.56-0.72], p = 0.012. Single cell transcriptomic data provide evidence that innate immune cells are the primary source of the identified proteins. CONCLUSIONS: CXCR3 ligands are associated with CD recurrence following ICR. Incorporation of novel blood-based candidate biomarkers may aid in identification of CD recurrence.

Prior Immunogenicity to Anti-TNF Biologics Is Not Associated with Increased Anti-drug Antibodies to Vedolizumab or Ustekinumab.

INTRODUCTION: Patients with inflammatory bowel disease (IBD) on biologic therapy may lose response to anti-tumor necrosis factor agents (anti-TNFs) due to the development of anti-drug antibodies (ADAs). A history of anti-TNF ADA increases the risk of developing ADA to subsequent anti-TNFs; however, it is not known whether ADA to anti-TNFs increases the risk of ADA development to vedolizumab (VDZ) or ustekinumab (UST). We aimed to investigate whether prior history of ADA to anti-TNF increases the risk of ADA to VDZ and UST. METHODS: We conducted a retrospective cohort study of patients at a tertiary care IBD center over the course of four years who had previous anti-TNF drug and ADA level data during maintenance treatment and subsequent VDZ or UST drug and antibody levels, all collected as standard of care. The primary outcome was the rate of ADA development to VDZ and UST in patients with and without prior anti-TNF immunogenicity. Descriptive statistics summarized the data and univariate tested associations. RESULTS: Of the 152 IBD patients analyzed, 41 (27%) had a history of previous anti-TNF ADA with 22 (53.7%) having simultaneously undetectable anti-TNF drug levels. There was no significant difference in the rates of ustekinumab and vedolizumab ADA development between patients with prior ADA and patients without prior ADA (1/41 [2.7%] vs 1/111 [0.9%]; p = 0.54). There was also no difference in concomitant immunomodulator use with ustekinumab or vedolizumab initiation in patients with or without prior ADA (13/41 [31.7%] vs 31/111 [27.9%], p = 0.84). Neither patient who developed ADA to VDZ or UST was on concomitant immunomodulator at drug initiation, and both patients had detectable drug levels at the time of antibody detection. CONCLUSIONS: We observed that prior immunogenicity to anti-TNF agents does not confer an increased risk of immunogenicity to ustekinumab or vedolizumab. Our data support the use of vedolizumab or ustekinumab as monotherapy for the treatment of IBD.

De Novo Ostomy Placement Is Associated with Increased Outpatient Opioid Use In Patients with Inflammatory Bowel Disease.

BACKGROUND/AIMS: Opioid use is associated with poor outcomes in patients with inflammatory bowel disease (IBD). We aimed to identify novel factors associated with increased outpatient opioid (OPRx) use following IBD-related hospitalization. METHODS: This was a retrospective cohort study of IBD patients ≥ 18 years old, hospitalized during 2018. The primary outcome was receiving ≥ 1(OPRx) in the year following index hospitalization (IH), excluding prescriptions written within 2 weeks of discharge. Secondary outcomes included having 1-2 vs ≥ 3 OPRx and rates of healthcare utilization. Univariate and multivariate analyses tested associations with OPRx. RESULTS: Of 526 patients analyzed, 209 (40%) received at least 1 OPRx; with a median of 2 [1-3] OPRx. Presence or placement of ostomy at IH, exposure to opioids during IH, ulcerative colitis (UC), mental health comorbidities, admission for surgery and managed on the surgical service, and IBD surgery within 1 year prior to IH were associated with ≥ 1 OPRx on univariate analysis. On multivariable analysis, UC, ostomy placement during IH, anxiety, and inpatient opioid exposure were independently associated with ≥ 1 OPRx. A majority (> 70%) of both inpatient and outpatient opioid prescriptions were written by surgeons. Patients requiring ≥ 3 OPRx had the highest rates of unplanned IBD surgery (56% p = 0.04), all-cause repeat hospitalization (81%, p = 0.003), and IBD-related repeat hospitalization (77%, p = 0.007) in the year following IH. CONCLUSIONS: A multimodal approach to pain management for IBD patients, as well as increased recognition that any patient with a de novo ostomy is at particular risk of opioid use, is needed.

A FLEXIBLE SENSITIVITY ANALYSIS APPROACH FOR UNMEASURED CONFOUNDING WITH MULTIPLE TREATMENTS AND A BINARY OUTCOME WITH APPLICATION TO SEER-MEDICARE LUNG CANCER DATA.

In the absence of a randomized experiment, a key assumption for drawing causal inference about treatment effects is the ignorable treatment assignment. Violations of the ignorability assumption may lead to biased treatment effect estimates. Sensitivity analysis helps gauge how causal conclusions will be altered in response to the potential magnitude of departure from the ignorability assumption. However, sensitivity analysis approaches for unmeasured confounding in the context of multiple treatments and binary outcomes are scarce. We propose a flexible Monte Carlo sensitivity analysis approach for causal inference in such settings. We first derive the general form of the bias introduced by unmeasured confounding, with emphasis on theoretical properties uniquely relevant to multiple treatments. We then propose methods to encode the impact of unmeasured confounding on potential outcomes and adjust the estimates of causal effects in which the presumed unmeasured confounding is removed. Our proposed methods embed nested multiple imputation within the Bayesian framework, which allow for seamless integration of the uncertainty about the values of the sensitivity parameters and the sampling variability, as well as use of the Bayesian Additive Regression Trees for modeling flexibility. Expansive simulations validate our methods and gain insight into sensitivity analysis with multiple treatments. We use the SEER-Medicare data to demonstrate sensitivity analysis using three treatments for early stage non-small cell lung cancer. The methods developed in this work are readily available in the R package SAMTx.

A proteogenomic portrait of lung squamous cell carcinoma.

Lung squamous cell carcinoma (LSCC) remains a leading cause of cancer death with few therapeutic options. We characterized the proteogenomic landscape of LSCC, providing a deeper exposition of LSCC biology with potential therapeutic implications. We identify NSD3 as an alternative driver in FGFR1-amplified tumors and low-p63 tumors overexpressing the therapeutic target survivin. SOX2 is considered undruggable, but our analyses provide rationale for exploring chromatin modifiers such as LSD1 and EZH2 to target SOX2-overexpressing tumors. Our data support complex regulation of metabolic pathways by crosstalk between post-translational modifications including ubiquitylation. Numerous immune-related proteogenomic observations suggest directions for further investigation. Proteogenomic dissection of CDKN2A mutations argue for more nuanced assessment of RB1 protein expression and phosphorylation before declaring CDK4/6 inhibition unsuccessful. Finally, triangulation between LSCC, LUAD, and HNSCC identified both unique and common therapeutic vulnerabilities. These observations and proteogenomics data resources may guide research into the biology and treatment of LSCC.

Estimating heterogeneous survival treatment effect in observational data using machine learning.

Methods for estimating heterogeneous treatment effect in observational data have largely focused on continuous or binary outcomes, and have been relatively less vetted with survival outcomes. Using flexible machine learning methods in the counterfactual framework is a promising approach to address challenges due to complex individual characteristics, to which treatments need to be tailored. To evaluate the operating characteristics of recent survival machine learning methods for the estimation of treatment effect heterogeneity and inform better practice, we carry out a comprehensive simulation study presenting a wide range of settings describing confounded heterogeneous survival treatment effects and varying degrees of covariate overlap. Our results suggest that the nonparametric Bayesian Additive Regression Trees within the framework of accelerated failure time model (AFT-BART-NP) consistently yields the best performance, in terms of bias, precision, and expected regret. Moreover, the credible interval estimators from AFT-BART-NP provide close to nominal frequentist coverage for the individual survival treatment effect when the covariate overlap is at least moderate. Including a nonparametrically estimated propensity score as an additional fixed covariate in the AFT-BART-NP model formulation can further improve its efficiency and frequentist coverage. Finally, we demonstrate the application of flexible causal machine learning estimators through a comprehensive case study examining the heterogeneous survival effects of two radiotherapy approaches for localized high-risk prostate cancer.

New frontiers against sorafenib resistance in renal cell carcinoma: From molecular mechanisms to predictive biomarkers.

Renal cell carcinoma (RCC) is a highly vascularized tumor and prone to distant metastasis. Sorafenib is the first targeted multikinase inhibitor and first-line chemical drug approved for RCC therapy. In fact, only a small number of RCC patients benefit significantly from sorafenib treatment, while the growing prevalence of sorafenib resistance has become a major obstacle for drug therapy effectivity of sorafenib. The molecular mechanisms of sorafenib resistance in RCC are not completely understood by now. Herein, we comprehensively summarize the underlying mechanisms of sorafenib resistance and molecular biomarkers for predicting sorafenib responsiveness. Moreover, we outline strategies suitable for overcoming sorafenib resistance and prospect potential approaches for identifying biomarkers associated with sorafenib resistance in RCC, which contributes to guide individualized and precision drug therapy.

Proteogenomic and metabolomic characterization of human glioblastoma.

Glioblastoma (GBM) is the most aggressive nervous system cancer. Understanding its molecular pathogenesis is crucial to improving diagnosis and treatment. Integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs provides insights to GBM biology. We identify key phosphorylation events (e.g., phosphorylated PTPN11 and PLCG1) as potential switches mediating oncogenic pathway activation, as well as potential targets for EGFR-, TP53-, and RB1-altered tumors. Immune subtypes with distinct immune cell types are discovered using bulk omics methodologies, validated by snRNA-seq, and correlated with specific expression and histone acetylation patterns. Histone H2B acetylation in classical-like and immune-low GBM is driven largely by BRDs, CREBBP, and EP300. Integrated metabolomic and proteomic data identify specific lipid distributions across subtypes and distinct global metabolic changes in IDH-mutated tumors. This work highlights biological relationships that could contribute to stratification of GBM patients for more effective treatment.

Proteogenomic insights into the biology and treatment of HPV-negative head and neck squamous cell carcinoma.

We present a proteogenomic study of 108 human papilloma virus (HPV)-negative head and neck squamous cell carcinomas (HNSCCs). Proteomic analysis systematically catalogs HNSCC-associated proteins and phosphosites, prioritizes copy number drivers, and highlights an oncogenic role for RNA processing genes. Proteomic investigation of mutual exclusivity between FAT1 truncating mutations and 11q13.3 amplifications reveals dysregulated actin dynamics as a common functional consequence. Phosphoproteomics characterizes two modes of EGFR activation, suggesting a new strategy to stratify HNSCCs based on EGFR ligand abundance for effective treatment with inhibitory EGFR monoclonal antibodies. Widespread deletion of immune modulatory genes accounts for low immune infiltration in immune-cold tumors, whereas concordant upregulation of multiple immune checkpoint proteins may underlie resistance to anti-programmed cell death protein 1 monotherapy in immune-hot tumors. Multi-omic analysis identifies three molecular subtypes with high potential for treatment with CDK inhibitors, anti-EGFR antibody therapy, and immunotherapy, respectively. Altogether, proteogenomics provides a systematic framework to inform HNSCC biology and treatment.

Genome-Wide Identification and Characterization of Calcium-Dependent Protein Kinase (CDPK) and CDPK-Related Kinase (CRK) Gene Families in Medicago truncatula.

Calcium-dependent protein kinase (CDPK or CPK) and CDPK-related kinase (CRK) play an important role in plant growth, development, and adaptation to environmental stresses. However, their gene families had been yet inadequately investigated in Medicago truncatula. In this study, six MtCRK genes were computationally identified, they were classified into five groups with MtCDPKs based on phylogenetic relationships. Six pairs of segmental duplications were observed in MtCDPK and MtCRK genes and the Ka/Ks ratio, an indicator of selection pressure, was below 0.310, indicating that these gene pairs underwent strong purifying selection. Cis-acting elements of morphogenesis, multiple hormone responses, and abiotic stresses were predicted in the promoter region. The spatial expression of MtCDPKs and MtCRKs displays diversity. The expression of MtCDPKs and MtCRKs could be regulated by various stresses. MtCDPK4, 14, 16, 22, and MtCRK6 harbor both N-myristoylation site and palmitoylation site and were anchored on plasma membrane, while MtCDPK7, 9, and 15 contain no or only one N-acylation site and were distributed in cytosol and nucleus, suggesting that the N-terminal acylation sites play a key role in subcellular localization of MtCDPKs and MtCRKs. In summary, comprehensive characterization of MtCDPKs and MtCRKs provide a subset of candidate genes for further functional analysis and genetic improvement against drought, cold, salt and biotic stress.