ProstaMine: a bioinformatics tool for identifying subtype-specific co-alterations associated with aggressiveness in prostate cancer.

Background: Prostate cancer is a leading cause of cancer-related deaths among men, marked by heterogeneous clinical and molecular characteristics. The complexity of the molecular landscape necessitates tools for identifying multi-gene co-alteration patterns that are associated with aggressive disease. The identification of such gene sets will allow for deeper characterization of the processes underlying prostate cancer progression and potentially lead to novel strategies for treatment. Methods: We developed ProstaMine to systematically identify co-alterations associated with aggressiveness in prostate cancer molecular subtypes defined by high-fidelity alterations in primary prostate cancer. ProstaMine integrates genomic, transcriptomic, and clinical data from five primary and one metastatic prostate cancer cohorts to prioritize co-alterations enriched in metastatic disease and associated with disease progression. Results: Integrated analysis of primary tumors defined a set of 17 prostate cancer alterations associated with aggressive characteristics. We applied ProstaMine to NKX3-1-loss and RB1-loss tumors and identified subtype-specific co-alterations associated with metastasis and biochemical relapse in these molecular subtypes. In NKX3-1-loss prostate cancer, ProstaMine identified novel subtype-specific co-alterations known to regulate prostate cancer signaling pathways including MAPK, NF-kB, p53, PI3K, and Sonic hedgehog. In RB1-loss prostate cancer, ProstaMine identified novel subtype-specific co-alterations involved in p53, STAT6, and MHC class I antigen presentation. Co-alterations impacting autophagy were noted in both molecular subtypes. Conclusion: ProstaMine is a method to systematically identify novel subtype-specific co-alterations associated with aggressive characteristics in prostate cancer. The results from ProstaMine provide insights into potential subtype-specific mechanisms of prostate cancer progression which can be formed into testable experimental hypotheses. ProstaMine is publicly available at: https://bioinformatics.cuanschutz.edu/prostamine.

NPEPPS Is a Druggable Driver of Platinum Resistance.

There is an unmet need to improve the efficacy of platinum-based cancer chemotherapy, which is used in primary and metastatic settings in many cancer types. In bladder cancer, platinum-based chemotherapy leads to better outcomes in a subset of patients when used in the neoadjuvant setting or in combination with immunotherapy for advanced disease. Despite such promising results, extending the benefits of platinum drugs to a greater number of patients is highly desirable. Using the multiomic assessment of cisplatin-responsive and -resistant human bladder cancer cell lines and whole-genome CRISPR screens, we identified puromycin-sensitive aminopeptidase (NPEPPS) as a driver of cisplatin resistance. NPEPPS depletion sensitized resistant bladder cancer cells to cisplatin in vitro and in vivo. Conversely, overexpression of NPEPPS in sensitive cells increased cisplatin resistance. NPEPPS affected treatment response by regulating intracellular cisplatin concentrations. Patient-derived organoids (PDO) generated from bladder cancer samples before and after cisplatin-based treatment, and from patients who did not receive cisplatin, were evaluated for sensitivity to cisplatin, which was concordant with clinical response. In the PDOs, depletion or pharmacologic inhibition of NPEPPS increased cisplatin sensitivity, while NPEPPS overexpression conferred resistance. Our data present NPEPPS as a druggable driver of cisplatin resistance by regulating intracellular cisplatin concentrations. SIGNIFICANCE: Targeting NPEPPS, which induces cisplatin resistance by controlling intracellular drug concentrations, is a potential strategy to improve patient responses to platinum-based therapies and lower treatment-associated toxicities.

Low nuclear expression of HIF-hydroxylases PHD2/EGLN1 and PHD3/EGLN3 are associated with poor recurrence-free survival in clear cell renal cell carcinoma.

BACKGROUND: Hypoxia inducible factors, HIF-1α and HIF-2α, and their main regulators, the prolyl hydroxylase domain proteins (PHDs), mediate cellular response to hypoxia and contribute to tumor progression in clear cell renal cell carcinoma (ccRCC). These biomarkers may improve the value of traditional histopathological features in predicting disease progression after nephrectomy for localized ccRCC and guide patient selection for adjuvant treatments. PATIENTS AND METHODS: In this study, we analyzed the associations of PHD2 and PHD3 with histopathological tumor features and recurrence-free survival (RFS) in a retrospective cohort of 173 patients who had undergone surgery for localized ccRCC at Helsinki University Hospital (HUH), Finland. An external validation cohort of 191 patients was obtained from Turku University Hospital (TUH), Finland. Tissue-microarrays (TMA) were constructed using the primary tumor samples. Clinical parameters and follow-up information from 2006 to 2019 were obtained from electronic medical records. The cytoplasmic and nuclear expression of PHD2, and PHD3 were scored based on immunohistochemical staining and their associations with histopathological features and RFS were evaluated. RESULTS: Nuclear PHD2 and PHD3 expression in cancer cells were associated with lower pT-stage and Fuhrman grade compared with negative nuclei. Patients with positive nuclear expression of PHD2 and PHD3 in cancer cells had favorable RFS compared with patients having negative tumors. The nuclear expression of PHD2 was independently associated with a decreased risk of disease recurrence or death from RCC in multivariable analysis. These results were observed in both cohorts. CONCLUSIONS: The absence of nuclear PHD2 and PHD3 expression in ccRCC was associated with poor RFS and the nuclear expression of PHD2 predicted RFS regardless of other known histopathological prognostic factors. Nuclear PHD2 and PHD3 are potential prognostic biomarkers in patients with localized ccRCC and should be further investigated and validated in prospective studies.

A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer.

BACKGROUND: Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. METHODS: Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. RESULTS: A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1. CONCLUSIONS: This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy. TRIAL REGISTRATION: CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.

Microbiome-based risk prediction in incident heart failure: a community challenge.

Heart failure (HF) is a major public health problem. Early identification of at-risk individuals could allow for interventions that reduce morbidity or mortality. The community-based FINRISK Microbiome DREAM challenge (synapse.org/finrisk) evaluated the use of machine learning approaches on shotgun metagenomics data obtained from fecal samples to predict incident HF risk over 15 years in a population cohort of 7231 Finnish adults (FINRISK 2002, n=559 incident HF cases). Challenge participants used synthetic data for model training and testing. Final models submitted by seven teams were evaluated in the real data. The two highest-scoring models were both based on Cox regression but used different feature selection approaches. We aggregated their predictions to create an ensemble model. Additionally, we refined the models after the DREAM challenge by eliminating phylum information. Models were also evaluated at intermediate timepoints and they predicted 10-year incident HF more accurately than models for 5- or 15-year incidence. We found that bacterial species, especially those linked to inflammation, are predictive of incident HF. This highlights the role of the gut microbiome as a potential driver of inflammation in HF pathophysiology. Our results provide insights into potential modeling strategies of microbiome data in prospective cohort studies. Overall, this study provides evidence that incorporating microbiome information into incident risk models can provide important biological insights into the pathogenesis of HF.

A harmonized resource of integrated prostate cancer clinical, -omic, and signature features.

Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or  not standardized. Here, we present the curatedPCaData R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. curatedPCaData is an open and accessible community resource with code made available for reproducibility.

The Effect of a Fish Oil and/or Probiotic Intervention from Early Pregnancy Onwards on Colostrum Immune Mediators: A Randomized, Placebo-Controlled, Double-Blinded Clinical Trial in Overweight/Obese Mothers.

SCOPE: Modifying the composition of colostrum by external factors may provide opportunities to improve the infant's health. Here, we evaluated how fish oil and/or probiotics supplementation modify concentrations of colostrum immune mediators and their associations with perinatal clinical factors on mothers with overweight/obesity. METHODS AND RESULTS: Pregnant women were randomized in a double-blind manner into four intervention groups, and the supplements were consumed daily from early pregnancy onwards. Colostrum samples were collected from 187 mothers, and 16 immune mediators were measured using bead-based immunoassays. Interventions modified colostrum composition; the fish oil+probiotics group had higher concentrations of IL-12p70 than probiotics+placebo and higher FMS-like tyrosine kinase 3 ligand (FLT-3L) than fish oil+placebo and probiotics+placebo (one-way analysis of variance, post-hoc Tukey's test). Although the fish oil+probiotics group had higher levels of IFNα2 compared to the fish oil+placebo group, these differences were not statistically significant after correction for multiple testing. Multivariate linear model revealed significant associations between several immune mediators and the perinatal use of medication. CONCLUSION: Fish oil/probiotics intervention exerted a minor effect on concentrations of colostrum immune mediators. However, medication during the perinatal period modulated the immune mediators. These changes in colostrum's composition may contribute to immune system development in the infant.

Non-muscle-invasive bladder cancer molecular subtypes predict differential response to intravesical Bacillus Calmette-Guérin.

The recommended treatment for patients with high-risk non-muscle-invasive bladder cancer (HR-NMIBC) is tumor resection followed by adjuvant Bacillus Calmette-Guérin (BCG) bladder instillations. However, only 50% of patients benefit from this therapy. If progression to advanced disease occurs, then patients must undergo a radical cystectomy with risks of substantial morbidity and poor clinical outcome. Identifying tumors unlikely to respond to BCG can translate into alternative treatments, such as early radical cystectomy, targeted therapies, or immunotherapies. Here, we conducted molecular profiling of 132 patients with BCG-naive HR-NMIBC and 44 patients with recurrences after BCG (34 matched), which uncovered three distinct BCG response subtypes (BRS1, 2 and BRS3). Patients with BRS3 tumors had a reduced recurrence-free and progression-free survival compared with BRS1/2. BRS3 tumors expressed high epithelial-to-mesenchymal transition and basal markers and had an immunosuppressive profile, which was confirmed with spatial proteomics. Tumors that recurred after BCG were enriched for BRS3. BRS stratification was validated in a second cohort of 151 BCG-naive patients with HR-NMIBC, and the molecular subtypes outperformed guideline-recommended risk stratification based on clinicopathological variables. For clinical application, we confirmed that a commercially approved assay was able to predict BRS3 tumors with an area under the curve of 0.87. These BCG response subtypes will allow for improved identification of patients with HR-NMIBC at the highest risk of progression and have the potential to be used to select more appropriate treatments for patients unlikely to respond to BCG.

OSCAR: Optimal subset cardinality regression using the L0-pseudonorm with applications to prognostic modelling of prostate cancer.

In many real-world applications, such as those based on electronic health records, prognostic prediction of patient survival is based on heterogeneous sets of clinical laboratory measurements. To address the trade-off between the predictive accuracy of a prognostic model and the costs related to its clinical implementation, we propose an optimized L0-pseudonorm approach to learn sparse solutions in multivariable regression. The model sparsity is maintained by restricting the number of nonzero coefficients in the model with a cardinality constraint, which makes the optimization problem NP-hard. In addition, we generalize the cardinality constraint for grouped feature selection, which makes it possible to identify key sets of predictors that may be measured together in a kit in clinical practice. We demonstrate the operation of our cardinality constraint-based feature subset selection method, named OSCAR, in the context of prognostic prediction of prostate cancer patients, where it enables one to determine the key explanatory predictors at different levels of model sparsity. We further explore how the model sparsity affects the model accuracy and implementation cost. Lastly, we demonstrate generalization of the presented methodology to high-dimensional transcriptomics data.

Antibiotic Treatment is an Independent Poor Risk Factor in NSCLC But Not in Melanoma Patients Who had Received Anti-PD-1/L1 Monotherapy.

BACKGROUND: Antibiotic treatment may reduce the efficacy of cancer immunotherapy by disrupting gut microbiome. We aimed to study the association of antibiotics and survival outcomes in advanced cutaneous melanoma and non-small-cell lung cancer (NSCLC) patients who had received anti-PD-1/L1 monotherapy. PATIENTS AND METHODS: A total of 222 melanoma and 199 NSCLC patients had received anti-PD-1/L1 monotherapy in 5 Finnish hospitals between January 2014 and December 2020. Clinical characteristics, antibiotic and corticosteroid treatment, and survival outcomes were retrospectively collected from hospital and national medical records. RESULTS: There were 32% of melanoma and 31% of NSCLC patients who had received antibiotic treatment (ABT) 3 months before to 1 month after the first anti-PD-1/L1 antibody infusion. In survival analyses, early antibiotic treatment was associated with inferior overall survival (OS) (ABT 19.2 [17.6-43.7] vs. no ABT 35.6 [29.3-NA] months, P = .033) but not with inferior progression-free survival (PFS) (ABT 5.8 [3.0-12.6] vs. no ABT 10.2 [7.7-15.3] months, P = .3) in melanoma patients and with inferior OS (ABT 8.6 [6.4-12.3] vs. no ABT 18.5 [15.1-21.6] months, P < .001) and PFS (ABT 2.8 [2.1-4.5] vs. no ABT 5.6 [4.4-8.0] months, P = .0081) in NSCLC patients. In multivariable analyses, ABT was not an independent risk-factor for inferior OS and PFS in melanoma but was associated with inferior OS (hazard ratio [HR] 2.12 [1.37-3.28]) and PFS (HR 1.65 [1.10-2.47]) in NSCLC after adjusted for other risk factors. CONCLUSIONS: Early ABT was an independent poor risk factor in NSCLC patients who had received anti-PD-1/L1 monotherapy but not in melanoma patients. The weight of ABT as a poor risk factor might depend on other prognostic factors in different cancers.

curatedPCaData: Integration of clinical, genomic, and signature features in a curated and harmonized prostate cancer data resource.

Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or unstandardized. Here, we present the curatedPCaData R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. curatedPCaData is an open and accessible community resource with code made available for reproducibility.

High intratumoral dihydrotestosterone is associated with antiandrogen resistance in VCaP prostate cancer xenografts in castrated mice.

Antiandrogen treatment resistance is a major clinical concern in castration-resistant prostate cancer (CRPC) treatment. Using xenografts of VCaP cells we showed that growth of antiandrogen resistant CRPC tumors were characterized by a higher intratumor dihydrotestosterone (DHT) concentration than that of treatment responsive tumors. Furthermore, the slow tumor growth after adrenalectomy was associated with a low intratumor DHT concentration. Reactivation of androgen signaling in enzalutamide-resistant tumors was further shown by the expression of several androgen-dependent genes. The data indicate that intratumor DHT concentration and expression of several androgen-dependent genes in CRPC lesions is an indication of enzalutamide treatment resistance and an indication of the need for further androgen blockade. The presence of an androgen synthesis, independent of CYP17A1 activity, has been shown to exist in prostate cancer cells, and thus, novel androgen synthesis inhibitors are needed for the treatment of enzalutamide-resistant CRPC tumors that do not respond to abiraterone.

Ovarian Cancers with Low CIP2A Tumor Expression Constitute an APR-246-Sensitive Disease Subtype.

Identification of ovarian cancer patient subpopulations with increased sensitivity to targeted therapies could offer significant clinical benefit. We report that 22% of the high-grade ovarian cancer tumors at diagnosis express CIP2A oncoprotein at low levels. Furthermore, regardless of their significantly lower likelihood of disease relapse after standard chemotherapy, a portion of relapsed tumors retain their CIP2A-deficient phenotype. Through a screen for therapeutics that would preferentially kill CIP2A-deficient ovarian cancer cells, we identified reactive oxygen species inducer APR-246, tested previously in ovarian cancer clinical trials. Consistent with CIP2A-deficient ovarian cancer subtype in humans, CIP2A is dispensable for development of MISIIR-Tag-driven mouse ovarian cancer tumors. Nevertheless, CIP2A-null ovarian cancer tumor cells from MISIIR-Tag mice displayed APR-246 hypersensitivity both in vitro and in vivo. Mechanistically, the lack of CIP2A expression hypersensitizes the ovarian cancer cells to APR-246 by inhibition of NF-κB activity. Accordingly, combination of APR-246 and NF-κB inhibitor compounds strongly synergized in killing of CIP2A-positive ovarian cancer cells. Collectively, the results warrant consideration of clinical testing of APR-246 for CIP2A-deficient ovarian cancer tumor subtype patients. Results also reveal CIP2A as a candidate APR-246 combination therapy target for ovarian cancer.

Androgen Receptor Signaling in Prostate Cancer Genomic Subtypes.

While many prostate cancer (PCa) cases remain indolent and treatable, others are aggressive and progress to the metastatic stage where there are limited curative therapies. Androgen receptor (AR) signaling remains an important pathway for proliferative and survival programs in PCa, making disruption of AR signaling a viable therapy option. However, most patients develop resistance to AR-targeted therapies or inherently never respond. The field has turned to PCa genomics to aid in stratifying high risk patients, and to better understand the mechanisms driving aggressive PCa and therapy resistance. While alterations to the AR gene itself occur at later stages, genomic changes at the primary stage can affect the AR axis and impact response to AR-directed therapies. Here, we review common genomic alterations in primary PCa and their influence on AR function and activity. Through a meta-analysis of multiple independent primary PCa databases, we also identified subtypes of significantly co-occurring alterations and examined their combinatorial effects on the AR axis. Further, we discussed the subsequent implications for response to AR-targeted therapies and other treatments. We identified multiple primary PCa genomic subtypes, and given their differing effects on AR activity, patient tumor genetics may be an important stratifying factor for AR therapy resistance.

A three-feature prediction model for metastasis-free survival after surgery of localized clear cell renal cell carcinoma.

After surgery of localized renal cell carcinoma, over 20% of the patients will develop distant metastases. Our aim was to develop an easy-to-use prognostic model for predicting metastasis-free survival after radical or partial nephrectomy of localized clear cell RCC. Model training was performed on 196 patients. Right-censored metastasis-free survival was analysed using LASSO-regularized Cox regression, which identified three key prediction features. The model was validated in an external cohort of 714 patients. 55 (28%) and 134 (19%) patients developed distant metastases during the median postoperative follow-up of 6.3 years (interquartile range 3.4-8.6) and 5.4 years (4.0-7.6) in the training and validation cohort, respectively. Patients were stratified into clinically meaningful risk categories using only three features: tumor size, tumor grade and microvascular invasion, and a representative nomogram and a visual prediction surface were constructed using these features in Cox proportional hazards model. Concordance indices in the training and validation cohorts were 0.755 ± 0.029 and 0.836 ± 0.015 for our novel model, which were comparable to the C-indices of the original Leibovich prediction model (0.734 ± 0.035 and 0.848 ± 0.017, respectively). Thus, the presented model retains high accuracy while requiring only three features that are routinely collected and widely available.

Exploratory Analysis of CA125-MGL and -STn Glycoforms in the Differential Diagnostics of Pelvic Masses.

BACKGROUND: The cancer antigen 125 (CA125) immunoassay (IA) does not distinguish epithelial ovarian cancer (EOC) from benign disease with the sensitivity needed in clinical practice. In recent studies, glycoforms of CA125 have shown potential as biomarkers in EOC. Here, we assessed the diagnostic abilities of two recently developed CA125 glycoform assays for patients with a pelvic mass. Detailed analysis was further conducted for postmenopausal patients with marginally elevated conventionally measured CA125 levels, as this subgroup presents a diagnostic challenge in the clinical setting. METHODS: Our study population contained 549 patients diagnosed with EOC, benign ovarian tumors, and endometriosis. Of these, 288 patients were postmenopausal, and 98 of them presented with marginally elevated serum levels of conventionally measured CA125 at diagnosis. Preoperative serum levels of conventionally measured CA125 and its glycoforms (CA125-MGL and CA125-STn) were determined. RESULTS: The CA125-STn assay identified EOC significantly better than the conventional CA125-IA in postmenopausal patients (85% vs. 74% sensitivity at a fixed specificity of 90%, P = 0.0009). Further, both glycoform assays had superior AUCs compared to the conventional CA125-IA in postmenopausal patients with marginally elevated CA125. Importantly, the glycoform assays reduced the false positive rate of the conventional CA125-IA. CONCLUSIONS: The results indicate that the CA125 glycoform assays markedly improve the performance of the conventional CA125-IA in the differential diagnosis of pelvic masses. This result is especially valuable when CA125 is marginally elevated.

Cost-effective survival prediction for patients with advanced prostate cancer using clinical trial and real-world hospital registry datasets.

INTRODUCTION: Predictive survival modeling offers systematic tools for clinical decision-making and individualized tailoring of treatment strategies to improve patient outcomes while reducing overall healthcare costs. In 2015, a number of machine learning and statistical models were benchmarked in the DREAM 9.5 Prostate Cancer Challenge, based on open clinical trial data for metastatic castration resistant prostate cancer (mCRPC). However, applying these models into clinical practice poses a practical challenge due to the inclusion of a large number of model variables, some of which are not routinely monitored or are expensive to measure. OBJECTIVES: To develop cost-specified variable selection algorithms for constructing cost-effective prognostic models of overall survival that still preserve sufficient model performance for clinical decision making. METHODS: Penalized Cox regression models were used for the survival prediction. For the variable selection, we implemented two algorithms: (i) LASSO regularization approach; and (ii) a greedy cost-specified variable selection algorithm. The models were compared in three cohorts of mCRPC patients from randomized clinical trials (RCT), as well as in a real-world cohort (RWC) of advanced prostate cancer patients treated at the Turku University Hospital. Hospital laboratory expenses were utilized as a reference for computing the costs of introducing new variables into the models. RESULTS: Compared to measuring the full set of clinical variables, economic costs could be reduced by half without a significant loss of model performance. The greedy algorithm outperformed the LASSO-based variable selection with the lowest tested budgets. The overall top performance was higher with the LASSO algorithm. CONCLUSION: The cost-specified variable selection offers significant budget optimization capability for the real-world survival prediction without compromising the predictive power of the model.

Publisher Correction: Pharmacological reactivation of MYC-dependent apoptosis induces susceptibility to anti-PD-1 immunotherapy.

The original version of this Article contained an error in Fig. 7. In panel b, the survival curves were shifted relative to the y axis. This error has been corrected in both the PDF and HTML versions of the Article.

Pharmacological reactivation of MYC-dependent apoptosis induces susceptibility to anti-PD-1 immunotherapy.

Elevated MYC expression sensitizes tumor cells to apoptosis but the therapeutic potential of this mechanism remains unclear. We find, in a model of MYC-driven breast cancer, that pharmacological activation of AMPK strongly synergizes with BCL-2/BCL-XL inhibitors to activate apoptosis. We demonstrate the translational potential of an AMPK and BCL-2/BCL-XL co-targeting strategy in ex vivo and in vivo models of MYC-high breast cancer. Metformin combined with navitoclax or venetoclax efficiently inhibited tumor growth, conferred survival benefits and induced tumor infiltration by immune cells. However, withdrawal of the drugs allowed tumor re-growth with presentation of PD-1+/CD8+ T cell infiltrates, suggesting immune escape. A two-step treatment regimen, beginning with neoadjuvant metformin+venetoclax to induce apoptosis and followed by adjuvant metformin+venetoclax+anti-PD-1 treatment to overcome immune escape, led to durable antitumor responses even after drug withdrawal. We demonstrate that pharmacological reactivation of MYC-dependent apoptosis is a powerful antitumor strategy involving both tumor cell depletion and immunosurveillance.

Modeling genetic heterogeneity of drug response and resistance in cancer.

Heterogeneity in tumors is recognized as a key contributor to drug resistance and spread of advanced disease, but deep characterization of genetic variation within tumors has only recently been quantifiable with the advancement of next generation sequencing and single cell technologies. These data have been essential in developing molecular models of how tumors develop, evolve, and respond to environmental changes, such as therapeutic intervention. A deeper understanding of tumor evolution has subsequently opened up new research efforts to develop mathematical models that account for evolutionary dynamics with the goal of predicting drug response and resistance in cancer. Here, we describe recent advances and limitations of how models of tumor evolution can impact treatment strategies for cancer patients.