Predicting immunotherapy response through genomics

Clinical and immunophenotype correlating with response to immunotherapy in paediatric patients with primary liver carcinoma. A case series

BACKGROUND

Paediatric hepatocellular carcinomas (HCC) traditionally arise in the context of a normal structural and functional liver and carry a dismal prognosis. While chemotherapy is the frontline standard, there is emerging interest in the study of immunotherapies for paediatric patients with relapsed/refractory disease. There is limited data to support whether immunotherapies will be of utility in this patient population.

METHODS

Six paediatric patients (median age:16 years, range: 12-17 at the time of treatment) with advanced hepatocellular neosplams, either conventional hepatocellular or fibrolamellar carcinoma, were treated with immunotherapy. Patients were consented to institutional genomic profiling and biobanking protocols. Baseline samples and serial tissue samples, when available, were evaluated for somatic mutation rate, actionable gene mutations, and pan-immune bulk RNA expression profiling. Results were correlated with clinical course.

FINDINGS

Three patients responded to checkpoint inhibition: one achieved a complete, durable response and the other two, prolonged stable disease. Three additional patients progressed. Diagnostic tissue from the complete responder demonstrated a higher relative mutational burden and robust immune infiltrate. Pre-treatment samples from the three responders demonstrated decreased expression of genes associated with T-cell dysfunction.

INTERPRETATION

A subset of patients with primary paediatric hepatocellular tumours will respond to immunotherapy. Immunotherapies are currently under prospective study for relapsed/refractory liver tumours in paediatric patients. Results from this report support the prospective collection of serial serum and tissue samples which may further identify genomic and immunophenotypic patterns predictive of response.

FUNDING

This work was supported by Philanthropic funds (Pan Mass Challenge, Team Angus and Team Perspective).

Hybridizing mechanistic mathematical modeling with deep learning methods to predict individual cancer patient survival after immune checkpoint inhibitor therapy

We present a study where predictive mechanistic modeling is used in combination with deep learning methods to predict individual patient survival probabilities under immune checkpoint inhibitor (ICI) therapy. This hybrid approach enables prediction based on both measures that are calculable from mechanistic models (but may not be directly measurable in the clinic) and easily measurable quantities or characteristics (that are not always readily incorporated into predictive mechanistic models). The mechanistic model we have applied here can predict tumor response from CT or MRI imaging based on key mechanisms underlying checkpoint inhibitor therapy, and in the present work, its parameters were combined with readily-available clinical measures from 93 patients into a hybrid training set for a deep learning time-to-event predictive model. Analysis revealed that training an artificial neural network with both mechanistic modeling-derived and clinical measures achieved higher per-patient predictive accuracy based on event-time concordance, Brier score, and negative binomial log-likelihood-based criteria than when only mechanistic model-derived values or only clinical data were used. Feature importance analysis revealed that both clinical and model-derived parameters play prominent roles in neural network decision making, and in increasing prediction accuracy, further supporting the advantage of our hybrid approach. We anticipate that many existing mechanistic models may be hybridized with deep learning methods in a similar manner to improve predictive accuracy through addition of additional data that may not be readily implemented in mechanistic descriptions.

Orchestrating smart therapeutics to achieve optimal treatment in small cell lung cancer: recent progress and future directions

Small cell lung cancer (SCLC) is a recalcitrant malignancy with elusive mechanism of pathogenesis and dismal prognosis. Over the past decades, platinum-based chemotherapy has been the backbone treatment for SCLC. However, subsequent chemoresistance after initial effectiveness urges researchers to explore novel therapeutic targets of SCLC. Recent years have witnessed significant improvements in targeted therapy in SCLC. New molecular candidates such as Ataxia telangiectasia and RAD3-related protein (ATR), WEE1, checkpoint kinase 1 (CHK1) and poly-ADP-ribose polymerase (PARP) have shown promising therapeutic utility in SCLC. While immune checkpoint inhibitor (ICI) has emerged as an indispensable treatment modality for SCLC, approaches to boost efficacy and reduce toxicity as well as selection of reliable biomarkers for ICI in SCLC have remained elusive and warrants our further investigation. Given the increasing importance of precision medicine in SCLC, optimal subtyping of SCLC using multi-omics have gradually applied into clinical practice, which may identify more drug targets and better tailor treatment strategies to each individual patient. The present review summarizes recent progress and future directions in SCLC. In addition to the emerging new therapeutics, we also focus on the establishment of predictive model for early detection of SCLC. More importantly, we also propose a multi-dimensional model in the prognosis of SCLC to ultimately attain the goal of accurate treatment of SCLC.

Biology of cancer; from cellular and molecular mechanisms to developmental processes and adaptation

Cancer research has been largely focused on the cellular and molecular levels of investigation. Recent data show that not only the cell but also the extracellular matrix plays a major role in the progression of malignancy. In this way, the cells and the extracellular matrix create a specific local microenvironment that supports malignant development. At the same time, cancer implies a systemic evolution which is closely related to developmental processes and adaptation. Consequently, there is currently a real gap between the local investigation of cancer at the microenvironmental level, and the pathophysiological approach to cancer as a systemic disease. In fact, the cells and the matrix are not only complementary structures but also interdependent components that act synergistically. Such relationships lead to cell-matrix integration, a supracellular form of biological organization that supports tissue development. The emergence of this supracellular level of organization, as a structure, leads to the emergence of the supracellular control of proliferation, as a supracellular function. In humans, proliferation is generally involved in developmental processes and adaptation. These processes suppose a specific configuration at the systemic level, which generates high-order guidance for local supracellular control of proliferation. In conclusion, the supracellular control of proliferation act as an interface between the downstream level of cell division and differentiation, and upstream level of developmental processes and adaptation. Understanding these processes and their disorders is useful not only to complete the big picture of malignancy as a systemic disease, but also to open new treatment perspectives in the form of etiopathogenic (supracellular or informational) therapies.

High Mutation Burden in ER-Positive/HER2-Negative/Luminal Breast Cancers

Background: Tumor mutation burden (TMB) is arising as a useful marker of checkpoint inhibitors’ effectiveness in cancer patients in general and has been proposed as predictive in breast cancers. Despite the initial success of checkpoint inhibitors in triple-negative breast cancer, ER-positive breast cancers are less amenable to immunotherapy treatments due to the lower immunogenicity of this subset, associated with lower TMB and less pronounced inflammatory cell infiltration. However, a minority of ER-positive breast cancers do have a higher TMB and could be targets of immune checkpoint inhibitors. Methods: This investigation uses publicly available genomic data to examine ER-positive/HER2-negative or luminal breast cancers with high mutation numbers and compare them with cancers of the same subtype and low mutation numbers. Clinical characteristics and molecular correlates according to mutation numbers are described. Results: ER-positive/HER2-negative and luminal breast cancers with high mutation numbers have a higher prevalence of PIK3CA mutations and in some of the series examined mutations in TP53 and CDH1. A significant proportion of cancers with high mutation numbers carry mutations in microsatellite instability genes and genes involved in DNA damage response. Despite these differences, the prognosis of ER-positive/HER2-negative and luminal breast cancers with high mutation numbers is not significantly different compared to counterparts with lower mutation counts. Conclusions: These data may inform the potential suitability of these cancers for immunotherapy and could guide the development of rational combination therapies based on immune checkpoint inhibitors with other targeted drugs.

Tumor in the Crossfire: Inhibiting TGF-β to Enhance Cancer Immunotherapy

Cancer immunotherapy using monoclonal antibodies targeting immune checkpoints has undoubtedly revolutionized the cancer treatment landscape in the last decade. Immune checkpoint inhibitors can elicit long-lasting, previously unheard-of responses in a number of tumor entities. Yet, even in such tumors as metastatic melanoma and non-small cell-lung cancer, in which immune checkpoint inhibition has become the first-line treatment of choice, only a minority of patients will benefit considerably from these treatments. This has been attributed to a number of factors, including an immune-suppressive tumor microenvironment (TME). Using different modalities to break these barriers is of utmost importance to expand the population of patients that benefit from immune checkpoint inhibition. The multifunctional cytokine transforming growth factor-β (TGF-β) has long been recognized as an immune-suppressive factor in the TME. A considerable number of drugs have been developed to target TGF-β, yet most of these have since been discontinued. The combination of anti-TGF-β agents with immune checkpoint inhibitors now has the potential to revive this target as a viable immunomodulatory therapeutic approach. Currently, a limited number of small molecular inhibitor and monoclonal antibody candidates that target TGF-β are in clinical development in combination with the following immune checkpoint inhibitors: SRK 181, an antibody inhibiting the activation of latent TGF-β1; NIS 793, a monoclonal antibody targeting TGF-β; and SHR 1701, a fusion protein consisting of an anti-PD-L1 monoclonal antibody fused with the extracellular domain of human TGF-β receptor II. Several small molecular inhibitors are also in development and are briefly reviewed: LY364947, a pyrazole-based small molecular inhibitor of the serine-threonine kinase activity of TGFβRI; SB-431542, an inhibitor targeting several TGF-β superfamily Type I activin receptor-like kinases as well as TGF-β1-induced nuclear Smad3 localization; and galunisertib, an oral small molecular inhibitor of the TGFβRI kinase. One of the most advanced agents in this area is bintrafusp alfa, a bifunctional fusion protein composed of the extracellular domain of TGF-β receptor II fused to a human IgG1 mAb blocking PD-L1. Bintrafusp alfa is currently in advanced clinical development and as an agent in this space with the most clinical experience, is a focused highlight of this review.

Urothelial Bladder Carcinomas with High Tumor Mutation Burden Have a Better Prognosis and Targetable Molecular Defects beyond Immunotherapies

Background: Urothelial bladder carcinomas had traditionally been difficult to treat cancers, with high morbidity and mortality rates when invasive and metastatic. In recent years, immunotherapy with immune checkpoint inhibitors has improved outcomes in several cancers, including bladder carcinomas. Despite positive overall results, many bladder cancer patients do not respond to immunotherapies. Validated predictive biomarkers of response would advance the selection of patients for these treatments. Tumor mutation burden (TMB) has been suggested as an immunotherapy biomarker and thus delineation of attributes of tumors with a high TMB is clinically relevant. Methods: Publicly available genomic and clinical data from the urothelial bladder carcinoma cohort of The Cancer Genome Atlas (TCGA) project are used to analyze characteristics and molecular alterations of the subset of cancers with an increased tumor mutation number compared with those with lower number of mutations. The cut-off for the high mutation burden in the analysis was set at 10 mutations per Megabase (MB). Results: In addition to their sensitivity to immune checkpoint inhibitors, urothelial carcinomas with high TMB possess several molecular defects that could be exploited for combinatorial treatments. Compared with bladder carcinomas with low TMB, carcinomas with high TMB display higher prevalence of mutations in tumor suppressor TP53, PIK3CA, in FAT4 cadherin and in genes encoding for several epigenetic modifier enzymes. The frequency of mutations in mismatch repair and DNA damage response genes is higher in cancers with high TMB. The group of urothelial carcinomas with high TMB has a better prognosis than the group with low TMB. This improved Overall Survival (OS) stems from improved survival of stage III cancers with high TMB compared with stage III cancers with low TMB, while stage II and stage IV cancers have similar OS, independently of their TMB. Conclusion: Differences of the landscape of high and low TMB urothelial cancers provides leads for further pathogenesis investigations and may prove useful for development of combination therapies including immunotherapies with targeted inhibitors.

Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling

Background:

Checkpoint inhibitor therapy of cancer has led to markedly improved survival of a subset of patients in multiple solid malignant tumor types, yet the factors driving these clinical responses or lack thereof are not known. We have developed a mechanistic mathematical model for better understanding these factors and their relations in order to predict treatment outcome and optimize personal treatment strategies.

Methods:

Here, we present a translational mathematical model dependent on three key parameters for describing efficacy of checkpoint inhibitors in human cancer: tumor growth rate (α), tumor-immune infiltration (Λ), and immunotherapy-mediated amplification of anti-tumor response (µ). The model was calibrated by fitting it to a compiled clinical tumor response dataset (n = 189 patients) obtained from published anti-PD-1 and anti-PD-L1 clinical trials, and then validated on an additional validation cohort (n = 64 patients) obtained from our in-house clinical trials.

Results:

The derived parameters Λ and µ were both significantly different between responding versus nonresponding patients. Of note, our model appropriately classified response in 81.4% of patients by using only tumor volume measurements and within 2 months of treatment initiation in a retrospective analysis. The model reliably predicted clinical response to the PD-1/PD-L1 class of checkpoint inhibitors across multiple solid malignant tumor types. Comparison of model parameters to immunohistochemical measurement of PD-L1 and CD8+ T cells confirmed robust relationships between model parameters and their underlying biology.

Conclusions:

These results have demonstrated reliable methods to inform model parameters directly from biopsy samples, which are conveniently obtainable as early as the start of treatment. Together, these suggest that the model parameters may serve as early and robust biomarkers of the efficacy of checkpoint inhibitor therapy on an individualized per-patient basis.

Funding:

We gratefully acknowledge support from the Andrew Sabin Family Fellowship, Center for Radiation Oncology Research, Sheikh Ahmed Center for Pancreatic Cancer Research, GE Healthcare, Philips Healthcare, and institutional funds from the University of Texas M.D. Anderson Cancer Center. We have also received Cancer Center Support Grants from the National Cancer Institute (P30CA016672 to the University of Texas M.D. Anderson Cancer Center and P30CA072720 the Rutgers Cancer Institute of New Jersey). This research has also been supported in part by grants from the National Science Foundation Grant DMS-1930583 (ZW, VC), the National Institutes of Health (NIH) 1R01CA253865 (ZW, VC), 1U01CA196403 (ZW, VC), 1U01CA213759 (ZW, VC), 1R01CA226537 (ZW, RP, WA, VC), 1R01CA222007 (ZW, VC), U54CA210181 (ZW, VC), and the University of Texas System STARS Award (VC). BC acknowledges support through the SER Cymru II Programme, funded by the European Commission through the Horizon 2020 Marie Skłodowska-Curie Actions (MSCA) COFUND scheme and the Welsh European Funding Office (WEFO) under the European Regional Development Fund (ERDF). EK has also received support from the Project Purple, NIH (U54CA210181, U01CA200468, and U01CA196403), and the Pancreatic Cancer Action Network (16-65-SING). MF was supported through NIH/NCI center grant U54CA210181, R01CA222959, DoD Breast Cancer Research Breakthrough Level IV Award W81XWH-17-1-0389, and the Ernest Cockrell Jr. Presidential Distinguished Chair at Houston Methodist Research Institute. RP and WA received serial research awards from AngelWorks, the Gillson-Longenbaugh Foundation, and the Marcus Foundation. This work was also supported in part by grants from the National Cancer Institute to SHC (R01CA109322, R01CA127483, R01CA208703, and U54CA210181 CITO pilot grant) and to PYP (R01CA140243, R01CA188610, and U54CA210181 CITO pilot grant). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Altered regulation of DPF3, a member of the SWI/SNF complexes, underlies the 14q24 renal cancer susceptibility locus

Our study investigated the underlying mechanism for the 14q24 renal cell carcinoma (RCC) susceptibility risk locus identified by a genome-wide association study (GWAS). The sentinel single-nucleotide polymorphism (SNP), rs4903064, at 14q24 confers an allele-specific effect on expression of the double PHD fingers 3 (DPF3) of the BAF SWI/SNF complex as assessed by massively parallel reporter assay, confirmatory luciferase assays, and eQTL analyses. Overexpression of DPF3 in renal cell lines increases growth rates and alters chromatin accessibility and gene expression, leading to inhibition of apoptosis and activation of oncogenic pathways. siRNA interference of multiple DPF3-deregulated genes reduces growth. Our results indicate that germline variation in DPF3, a component of the BAF complex, part of the SWI/SNF complexes, can lead to reduced apoptosis and activation of the STAT3 pathway, both critical in RCC carcinogenesis. In addition, we show that altered DPF3 expression in the 14q24 RCC locus could influence the effectiveness of immunotherapy treatment for RCC by regulating tumor cytokine secretion and immune cell activation.

Progressive Sarcopenia Correlates with Poor Response and Outcome to Immune Checkpoint Inhibitor Therapy

BACKGROUND

Immune checkpoint inhibitors (ICIs) represent a new therapeutic standard for an increasing number of tumor entities. Nevertheless, individual response and outcome to ICI is very heterogeneous, and the identification of the ideal ICI candidate has remained one of the major issues. Sarcopenia and the progressive loss of muscle mass and strength, as well as muscular fat deposition, have been established as negative prognostic factors for a variety of diseases, but their role in the context of ICI therapy is not fully understood. Here, we have evaluated skeletal muscle composition as a novel prognostic marker in patients undergoing ICI therapy for solid malignancies.

METHODS

We analyzed patients with metastasized cancers receiving ICI therapy according to the recommendation of the specific tumor board. Routine CT scans before treatment initialization and during ICI therapy were used to assess the skeletal muscle index (L3SMI) as well as the mean skeletal muscle attenuation (MMA) in n = 88 patients receiving ICI therapy.

RESULTS

While baseline L3SMI and MMA values were unsuitable for predicting the individual response and outcome to ICI therapy, longitudinal changes of the L3SMI and MMA (∆L3SMI, ∆MMA) during ICI therapy turned out to be a relevant marker of therapy response and overall survival. Patients who responded to ICI therapy at three months had a significantly higher ∆L3SMI compared to non-responders (-3.20 mm²/cm vs. 1.73 mm²/cm, p = 0.002). Moreover, overall survival (OS) was significantly lower in patients who had a strongly decreasing ∆L3SMI (<-6.18 mm²/cm) or a strongly decreasing ∆MMA (<-0.4 mm²/cm) during the first three month of ICI therapy. Median OS was only 127 days in patients with a ∆L3SMI of below -6.18 mm²/cm, compared to 547 days in patients with only mildly decreasing or even increasing ∆L3SMI values (p < 0.001).

CONCLUSION

Both progressive sarcopenia and an increasing skeletal muscle fat deposition are associated with poor response and outcome to ICI therapy, which might help to guide treatment decisions during ICI therapy.

Identification of a prognostic signature based on immune-related genes in bladder cancer

Bladder cancer (BLCA) has a high incidence and recurrence rate, and the effect of immunotherapy varies from person to person. Immune-related genes (IRGs) have been shown to be associated with immunotherapy and prognosis in many other cancers, but their role in immunogenic BLCA is less well defined. In this study, we constructed an eight-IRG risk model, which demonstrated strong prognostic and immunotherapeutic predictive power. The signature was significantly related to tumor clinicopathological characteristics, tumor class, immune cell infiltration and mutation status. Additionally, a nomogram containing the risk score and other potential risk factors could effectively predict the long-term overall survival probability of BLCA patients. The enriched mechanisms identified by gene set enrichment analysis suggested that the reason why this signature can accurately distinguish high- and low-risk populations may be closely related to the different degrees of innate immune response and T cell activation in different patients.