Abstract only
9117
Background: The Cellworks Singula Therapeutic Response Index (TRI) has been developed to assist clinicians and NSCLC patients in choosing between competing therapeutic options. In ...contrast to approaches that consider single aberrations, which often yield limited benefit, Cellworks utilizes an individual patient’s next generation sequencing results and a mechanistic multi-omics biology model, the Cellworks Omics Biology Model (CBM), to biosimulate downstream molecular effects of cell signaling, drugs, and radiation on patient-specific in silico diseased cells. For any individual patient and alternative therapy, Cellworks integrates this biologically modeled multi-omics information into a continuous Singula TRI Score, scaled from 0 (low therapeutic benefit) to 100 (high therapeutic benefit). We demonstrate that Singula is strongly associated with overall survival, progression-free survival and relative therapeutic benefit beyond standard clinical factors, including patient age, gender, and physician prescribed treatments (PPT). Methods: In this study, Singula’s ability to predict response was evaluated in a retrospective cohort of 446 NSCLC patients with OS, PFS, and CR data from The Cancer Genome Atlas (TCGA) project, treated with PPT. As a primary analysis of the CBM and TRI Score, Cox Proportional Hazards (PH) regression and likelihood ratio (LR) tests were used to assess the hypothesis that Singula is predictive of OS, PFS, and CR above and beyond standard clinical factors. A p-value < 0.05 for the corresponding likelihood ratio statistic was required to be considered significant. Results: Multivariate analyses were performed to assess the performance of the Singula Therapy Response Index above and beyond physician’s choice of treatment. The same Singula TRI algorithm and clinical cutoffs were used for all clinical outcome measures. For OS the median survival times for the high and low benefit groups were 60.16 and 28.57 months respectively, based on the median Singula value. Also, the hazard ratio per 25 Singula units for OS was 0.5103 (95% CI: 0.3337 - 0.7804) and the odds ratio for CR was 1.6161. These and further analyses, shown in Table, suggest that Singula TRI provides predictive value of OS, PFS, and CR above and beyond standard clinical factors. Conclusions: The Singula TRI Score provides a continuous measure for alternative NSCLC therapeutic options. In this retrospective cohort, Singula was strongly predictive of OS, PFS, and CR and provided predictive value of OS beyond PPT, patient age and gender. These results will be further validated in prospective clinical studies.Table: see text
4064
Background: Computational biological modeling reveals many dysregulated signaling pathways responsible for hallmark behaviors of cancer and variable drug response in the population. A ...mechanistic model created for each patient using comprehensive genomic inputs can biosimulate downstream molecular effects of cell signaling and drugs for each patient’s personalized in silico virtual disease model. Singula TRI is designed to predict the outcome of specific therapies with a continuous TRI Score, 0 to 100, for each patient’s unique genomic network. Methods: TRI’s ability to predict Overall Survival (OS), Disease Free Survival (DFS) and Mandard – tumor regression grade (TRG) was prospectively evaluated in a retrospective cohort of gastroesophageal adenocarcinoma (GEA) from UK OCCAMS consortium. Random sampling stratified by clinical factors was used to split the data into independent training (N = 140) and validation (N = 131) subsets. Multivariate Cox Proportional Hazard (PH) and Proportional Odds models were used to predict survival and pathological response as a function of the pre-defined TRI and clinical thresholds compared with standard clinical factors. Results: 271 GEA patients were selected who had pre-chemo treated biopsies with 50x whole genome sequencing from the OCCAMS International Cancer Genome Consortium study. The median age was 65.6 years, 234 male and 30 female, with deceased median OS of 21.9 months and living of 49.9 months. There were 35 T2, 215 T3, 70 N0, 126 N1, 62 N2 and 266 M0. Patients were treated with physician prescribed chemotherapy treatments (PPT) according to UK clinical guidelines (SC). Biosimulation revealed that 99% of these tumors had deficiency in DNA repair genes. Other pathways included amplification of multi-drug resistance pumps, TP53 mutations and aberrations of the PI3K/AKT pathway genes. The table shows that TRI provides additional predictive information for OS and DFS beyond PPT and standard clinical factors. TRI was also predictive of TRG in univariate analysis. TRI scores were also generated for 82 alternate therapies for each patient enabling selection of optimal therapies with estimates of improvements in median OS and DFS compared to SC. Conclusions: In this cohort of patients, Cellworks Singula TRI was predictive of survival and TRG beyond clinical factors. These positive results suggest the utility of biosimulation-informed therapy selection to improve survival of GEA and validation in prospective clinical studies is warranted.Table: see text
2053
Background: Comprehensive molecular profiling reveals significant differences in treatment response among GBM patients. A mechanistic multi-omics biology model allows biosimulation of molecular ...effects of cell signaling, drugs and radiation on patient-specific in silico diseased cells. The Cellworks Singula Therapy Response Index (TRI) identifies the magnitude of disease control and survival for specific anti-tumor strategies. TRI ranks the anticipated outcome of each therapy with a continuous TRI Score, 0 to 100, for each patient’s unique genomic network. Methods: TRI’s ability to predict OS and PFS was prospectively evaluated in a retrospective cohort of 270 IDH wildtype GBM patients from The Cancer Genome Atlas (TCGA) with known clinical outcomes treated with physician prescribed therapies (PPT). The median age was 57.5 years for 162 males and 108 females. There were 73 MGMT methylated with median OS deceased of 17.1 months and living of 9.5 months and median PFS of 6.5 months. There were 197 MGMT unmethylated with median OS deceased of 14.0 months and living of 13.6 months and median PFS of 6.0 months. Stratified random sampling was used to split the data into independent training (N = 153) and validation (N = 117) subjects. Multivariate Cox Proportional Hazard and Proportional Odds models were used to model OS and PFS as a function of the pre-defined Singula TRI and clinical thresholds. Cox Proportional Hazards (PH) regression and likelihood ratio (LR) tests were used on the independent validation subjects to assess the hypothesis that Singula is predictive of OS and PFS above and beyond standard clinical factors. Results: In the validation set, Singula TRI was significantly predictive of OS and PFS in univariate analyses and remained significantly predictive in multivariate analyses which included age, sex, MGMT methylation status and drug class. Singula TRI facilitates selection of optimal personalized therapies by providing patient-specific estimates of OS and PFS for 18 NCCN guideline GBM therapies. Conclusions: Cellworks Singula was strongly predictive of OS and PFS and provided predictive value beyond physician prescribed therapy, patient age, sex and MGMT methylation status. This information may be used to estimate increases in OS and PFS when comparing Singula TRI recommended therapies versus standard care. These positive results suggest the utility of biosimulation-informed therapy selection to improve survival of GBM and merits validation in prospective clinical studies. Table: see text
Background: Although some genomic biomarkers have been integrated into therapeutic decision-making for the management of AML, the complete remission and cure rates have significant margin for ...improvement. Except for a few targeted therapies, genomic assessments offer limited guidance on treatment. Nevertheless, comprehensive molecular profiling of AML discloses a complex and heterogeneous disease network that impacts the efficacy of individual chemotherapeutics differently in individual patients. The Cellworks Computational Omics Biology Model (CBM) was developed using artificial intelligence heuristics and literature sourced from PubMed to generate a patient-specific protein network map. The Cellworks Biosimulation Platform uses the CBM to model each patient's unique cancer and predict personalized responses to standard AML drugs, identify novel drug combinations for treatment-refractory patients and optimize treatment selection to improve outcomes.
Methods: A prospectively designed study involving observational data from 416 de novo AML patients was used to test the hypothesis that biosimulation using the Cellworks Biosimulation Platform predicts clinical response to individual drugs and estimates likelihood of response and survival better than physician prescribed treatment (PPT) alone. Cytogenetic and molecular data obtained from clinical trials including AMLSG 07-04, Beat AML, TCGA and PubMed publications was used to create personalized in silico models of each patient's AML and generate a Singula™ biosimulation report with a Therapy Response Index (TRI) to determine the efficacy of specific chemotherapeutic agents. The impact of specific AML agents on each patient's disease network was biosimulated to determine a treatment efficacy score by estimating the effect of chemotherapy on the cell growth score, a composite of cell proliferation, viability, apoptosis, metastasis, DNA damage and other cancer hallmarks. The mechanism of action of each drug was mapped to each patient's genome and biological consequences determined response. Multivariate logistic regression models for clinical response and likelihood ratio tests were used to assess the contribution of the Cellworks Biosimulation Platform beyond PPT. Similarly, multivariate Cox proportional hazards models were used to test the hypothesis that the Cellworks Biosimulation Platform is predictive of overall survival (OS) and provides predictive information beyond PPT alone. Scoring quantifies the benefit of each drug used to treat each patient's AML. Kaplan-Meier curves, associated log rank tests, and median OS are provided for patients predicted by predefined low and high treatment benefit groups.
Results: The TRI Score, scaled from 0 to 100, predicted complete response (CR) (likelihood ratio χ 12 = 52.54, p < 0.0001). Specific leukemia therapies generated a variable likelihood of benefit for individual patients. Notably, Cellworks biosimulation was able to predict treatment benefit or failure better than PPT alone (likelihood ratio χ 12 = 14.86, p < 0.0001). The use of therapy biosimulation to select therapy is estimated to increase the odds of CR by 19% per every 25 units of the TRI Score. TRI was also a significant predictor of OS (likelihood ratio χ 12 = 80.41, p < 0.0001) and provides predictive information above and beyond PPT alone (likelihood ratio χ 12 = 58.70, p < 0.0001 ). Inclusion of the Cellworks Biosimulation Platform is estimated to reduce the hazard ratio for death above and beyond PPT alone by 16% per every 25 units of the TRI Score. Furthermore, predictiveness curves suggest that approximately 25% of de novo AML patients had low probabilities of CR resulting in lower OS and could benefit substantially from inclusion of drugs and combinations identified by biosimulation into frontline management.
Conclusions: By predicting the impact of aberrations and copy number alterations on drug response, the Cellworks Biosimulation Platform can improve treatment outcomes for AML patients. The Cellworks TRI predicts response and OS beyond PPT alone, and the Cellworks Biosimulation Platform provides individualized, networked-based alternate treatment options for patients predicted to be non-responders to standard care.
Howard: Sanofi: Consultancy, Other: Speaker fees; Cellworks Group Inc.: Consultancy; Servier: Consultancy. Watson: Cellworks Group Inc.: Consultancy, Other: Advisor; CellMax Life: Consultancy, Other: Advisor; AlloVir: Consultancy, Membership on an entity's Board of Directors or advisory committees; BioAi Health: Consultancy, Membership on an entity's Board of Directors or advisory committees. Castro: Cellworks Group Inc.: Current Employment; Bugworks: Consultancy; Guardant Health Inc.: Speakers Bureau; Exact sciences Inc.: Consultancy; Caris Life Sciences Inc.: Consultancy; Omicure Inc: Consultancy. Kapoor: Cellworks Group Inc.: Current Employment. Nair: Cellworks Group Inc.: Current Employment. Prasad: Cellworks Group Inc.: Current Employment. Rajagopalan: Cellworks Group Inc.: Current Employment. Alam: Cellworks Group Inc.: Current Employment. Roy: Cellworks Group Inc.: Current Employment. Sahu: Cellworks Group Inc.: Current Employment. Lala: Cellworks Group Inc.: Current Employment. Basu: Cellworks Group Inc.: Current Employment. Ullal: Cellworks Group Inc.: Current Employment. Narvekar: Cellworks Group Inc.: Current Employment. Ghosh: Cellworks Group Inc.: Current Employment. Sauban: Cellworks Group Inc.: Current Employment. G: Cellworks Group Inc.: Current Employment. Agrawal: Cellworks Group Inc.: Current Employment. Tyagi: Cellworks Group Inc.: Current Employment. Suseela: Cellworks Group Inc.: Current Employment. Raju: Cellworks Group Inc.: Current Employment. Pampana: Cellworks Group Inc.: Current Employment. Patel: Cellworks Group Inc.: Current Employment. Mundkur: Cellworks Group Inc: Current Employment. Christie: Cellworks Group Inc.: Current Employment. Macpherson: Cellworks Group Inc.: Current Employment. Marcucci: Agios: Other: Speaker and advisory scientific board meetings; Novartis: Other: Speaker and advisory scientific board meetings; Abbvie: Other: Speaker and advisory scientific board meetings.
Background: DNA methyltransferase inhibition (DNMTi) with hypomethylating agents (HMA), azacitidine (AZA) or decitabine (DAC), remains the mainstay of therapy for most high-risk Myelodysplastic ...syndrome (MDS) patients. However, only 40-50% of MDS patients achieve clinical improvement with DNMTi. Previously, combinations of HMA and histone deacetylase (HDAC) inhibitors have been explored in MDS with varying clinical outcomes. However, the heterogeneity of genomic aberrations in MDS portend widely divergent responses from HDAC inhibition, implying that a predictive clinical decision support tool could select patients most likely to benefit from this combination. We explored the molecular basis of observed clinical response in a group of patients treated with DAC and Valproic-Acid (VPA).
Method: 16 MDS patients with known clinical responses to DAC + VPA were selected for study from the Cellworks patient repository. The aberration and copy number variations from individual cases served as input into the Computational Omics Biology Model, a computational multi-omic biology software model largely created using literature sourced from PubMed, to generate a patient-specific protein network map. Disease biomarkers unique to each patient were identified within these maps. The Cellworks Biosimulation Platform has the capacity to biosimulate disease phenotypic behavior and was used to create a patient-specific disease model. Biosimulations were then conducted on each patient-specific disease model to measure the effect of DAC + VPA according to a cell growth score. This score was comprised of a composite of cell proliferation, viability, apoptosis, metastasis, and other cancer hallmarks. Biosimulation of drug response was conducted to identify and predict therapeutic efficacy.
Results: In the biosimulation, VPA is a relatively weak HDAC inhibitor, but it also inhibits GSK3B and in turn increases beta-catenin (CTNNB1) levels. Additionally, monosomy 7 associated with loss of CAV1, HIPK2, and TRRAP also causes high CTNNB1, thereby further contributing to drug resistance. Biosimulation correctly identified that 7 of 8 patients with these genomic findings were clinical non-responders (NR) to VPA, indicating that CTNNB1 status is likely to predict treatment failure from the VPA + HMA combination in this disease. Notably, CTNNB1 levels have been reported to foster an immune-evasive tumor microenvironment resistant to CTL activation.
By contrast, high levels of c-MYC predict response to VPA + HMA combination. VPA inhibits MYC transcription and thereby reduces MYC-induced downregulation of p21 through CKS1B. Additionally MYC is a transcriptional regulator of DNMT1 which is degraded after hyperacetylation induced by HDAC3 inhibition suggesting that VPA also enhances DNMT1 turnover. One patient analyzed had trisomy 8 resulting in c-MYC over-expression and responded to HMA + VPA. Additionally, other aberrations enhancing c-MYC transcription such as copy number variant (CNV) loss of MXI1, HHEX, FBXW7, SMAD7 or CNV gain of BRD4, BCL7B led to high clinical response to the combination (Table 1). By comparison to the CTNNB1-driven subset, the impact of VPA on CTNNB1 in the MYC-dominant disease network did not negate the benefit of VPA for these patients. Additionally, the inhibition of GSK3B by VPA leading to diminished FBXW7 and less ubiquitin-mediated turnover of c-MYC was not sufficient to overcome the inhibition of MYC transcription and HDAC3i-mediated turnover.
Immune activation has become a recognized mechanism of responsiveness to HMA. However, among patients with upregulated CTNNB1, VPA is likely to further decrease response to treatment. By contrast, among MYC-driven cancers that are typically immune-evasive, VPA appears to be a vital mechanism of overcoming MYC-driven immune evasion.
Conclusion: Signaling pathway consequences related to CTNNB1 and c-MYC upregulation predict response to DAC + VPA. Although HMA plus HDAC inhibition can be generally beneficial for MDS, variable mechanisms of action among various HDAC inhibitors and unique patient disease characteristics should be considered for optimal treatment selection. Finally, CTNNB1 emerged from the Cellworks biosimulations as a therapeutically relevant target in MDS that determines whether VPA synergizes or antagonizes the effect of other agents in this challenging subtype of MDS.
Display omitted
Castro: Caris Life Sciences Inc.: Consultancy; Omicure Inc: Consultancy; Cellworks Group Inc.: Current Employment; Exact sciences Inc.: Consultancy; Guardant Health Inc.: Speakers Bureau; Bugworks: Consultancy. Kumar: Cellworks Group Inc.: Current Employment. Grover: Cellworks Group Inc.: Current Employment. Patil: Cellworks Group Inc.: Current Employment. Kapoor: Cellworks Group Inc.: Current Employment. Agrawal: Cellworks Group Inc.: Current Employment. Sauban: Cellworks Group Inc.: Current Employment. Prasad: Cellworks Group Inc.: Current Employment. Basu: Cellworks Group Inc.: Current Employment. Suseela: Cellworks Group Inc.: Current Employment. Kumar: Cellworks Group Inc.: Current Employment. Nair: Cellworks Group Inc.: Current Employment. Kumari: Cellworks Group Inc.: Current Employment. Pampana: Cellworks Group Inc.: Current Employment. Ullal: Cellworks Group Inc.: Current Employment. Azam: Cellworks Group Inc.: Current Employment. Prasad: Cellworks Group Inc.: Current Employment. Amara: Cellworks Group Inc.: Current Employment. Sahu: Cellworks Group Inc.: Current Employment. Raveendaran: Cellworks Group Inc.: Current Employment. Veedu: Cellworks Group Inc.: Current Employment. Mundkur: Cellworks Group Inc: Current Employment. Patel: Cellworks Group Inc.: Current Employment. Christie: Cellworks Group Inc.: Current Employment. Macpherson: Cellworks Group Inc.: Current Employment. Howard: Servier: Consultancy; Cellworks Group Inc.: Consultancy; Sanofi: Consultancy, Other: Speaker fees.
Background: DNA methyltransferase inhibition (DNMTi) with the hypomethylating agents (HMA) azacitidine (AZA) or decitabine, remains the mainstay of therapy for the majority of high-risk ...Myelodysplastic Syndromes (MDS) patients. Nevertheless, only 40-50% of MDS patients achieve clinical improvement with DNMTi. There is a need for a predictive clinical approach that can stratify MDS patients according to their chance of benefit from current therapies and that can identify and predict responses to new treatment options. Ideally, patients predicted to be non-responders (NR) could be offered alternative strategies while being spared protracted treatment with HMA alone that has a low likelihood of efficacy. Recently, an intriguing discovery of immune modulation by HMA has emerged. In addition to the benefits of unsilencing differentiation genes and tumor suppressor genes, HMA's reactivate human endogenous retroviral (HERV) genes leading to viral mimicry and upregulation of the immune response as a major mechanism of HMA efficacy. Although the PD-L1/PD1 blockade plus HMA has been recognized as a beneficial combination, there are no established markers to guide decision-making. We report here the utility of immunomic profiling of chromosome 9 copy number status as a significant mechanism of immune evasion and HMA resistance.
Methods: 119 patients with known clinical responses to AZA were selected for this study. Publicly available data largely from TCGA and PubMed was utilized for this study. The aberration and copy number variations from individual cases served as input into the Cellworks Computation Omics Biology Model (CBM), a computational biology multi-omic software model, created using artificial intelligence heuristics and literature sourced from PubMed, to generate a patient-specific protein network map. Disease-biomarkers unique to each patient were identified within protein network maps. The Cellworks Biosimulation Platform has the capacity to biosimulate disease phenotypic behavior and was used to create a disease model and then conduct biosimulations to measure the effect of AZA on a cell growth score comprised of a composite of cell proliferation, viability, apoptosis, metastasis, and other cancer hallmarks. Biosimulation of drug response was conducted to identify and predict therapeutic efficacy.
Results: Although AZA treatment increased tumor associated antigens and interferon signaling, it also increased PD-L1 expression to inactivate cytotoxic CD8(+) T cells. Copy number alterations of the chromosome 9p region were found to significantly drive PD-L1 expression with multiple genes such as CD274, IFNA1, IFNA2, JAK2, PDCD1LG and KDM4C playing a role in PD-L1 regulation further increasing immune suppression (Figure 1). Among 6 cases of chromosome 9p aberration in this dataset, 9p amp (n=2) were clinical non-responders (NR) while 9p del (n=4) were responders (R) to AZA. In principle, checkpoint immunotherapy could improve outcomes for patients with 9p abnormalities. Additionally, copy number variation loss of key genes located on chromosome 16 involved in antigen processing and presentation such as CIITA, CTCF, IRF8, PSMB10, NLRC5, and SOCS1 were found to negatively impact AZA sensitivity (NR=4; R=0); these patients would also be unlikely to respond to checkpoint immunotherapy. Also, aberrations in melanoma antigen gene (MAGE) family proteins (NR=2; R=O), and STT3A (NR=1; R=5) were found to impact AZA efficacy by decreasing antigen processing on tumor cells.
Conclusion: Based on the results from the Cellworks Biosimulation Platform applied to the CBM, copy number variants of chromosome 9p and 16 can be converted into CBM-derived biomarkers for response to checkpoint immunotherapy in combination with HMA. Our results support a future prospective evaluation in larger cohorts of MDS patients.
Display omitted
Howard: Servier: Consultancy; Cellworks Group Inc.: Consultancy; Sanofi: Consultancy, Other: Speaker fees. Kumar: Cellworks Group Inc.: Current Employment. Castro: Bugworks: Consultancy; Exact sciences Inc.: Consultancy; Guardant Health Inc.: Speakers Bureau; Cellworks Group Inc.: Current Employment; Caris Life Sciences Inc.: Consultancy; Omicure Inc: Consultancy. Grover: Cellworks Group Inc.: Current Employment. Mohapatra: Cellworks Group Inc.: Current Employment. Kapoor: Cellworks Group Inc.: Current Employment. Tyagi: Cellworks Group Inc.: Current Employment. Nair: Cellworks Group Inc.: Current Employment. Suseela: Cellworks Group Inc.: Current Employment. Pampana: Cellworks Group Inc.: Current Employment. Lala: Cellworks Group Inc.: Current Employment. Singh: Cellworks Group Inc.: Current Employment. Shyamasundar: Cellworks Group Inc.: Current Employment. Kulkarni: Cellworks Group Inc.: Current Employment. Narvekar: Cellworks Group Inc.: Current Employment. Sahni: Cellworks Group Inc.: Current Employment. Raman: Cellworks Group Inc.: Current Employment. Balakrishnan: Cellworks Group Inc.: Current Employment. Patil: Cellworks Group Inc.: Current Employment. Palaniyeppa: Cellworks Group Inc.: Current Employment. Balla: Cellworks Group Inc.: Current Employment. Patel: Cellworks Group Inc.: Current Employment. Mundkur: Cellworks Group Inc: Current Employment. Christie: Cellworks Group Inc.: Current Employment. Macpherson: Cellworks Group Inc.: Current Employment. Marcucci: Abbvie: Other: Speaker and advisory scientific board meetings; Novartis: Other: Speaker and advisory scientific board meetings; Agios: Other: Speaker and advisory scientific board meetings.
Abstract only
e21208
Background: Paclitaxel and carboplatin (PC) is used to treat a wide variety of malignancies including gynecologic, breast, lung, and occult primary cancers. In NSCLC, PC led to a ...substantial improvement in 1-yr survival from 10% (P alone) to approximately 50% seen with the combination. Nevertheless, a large proportion of patients do not respond. An optimal cytotoxic strategy for managing NSCLC and the discovery of chemotherapy biomarkers to guide treatment selection remain unmet needs in the clinic. Cellworks CBM platform identified a unique chromosomal signature which permits a stratification of which patients are most likely to respond to PC treatment. Methods: 22 patients treated with PC were published in TCGA dataset and selected for analysis. The mutation and copy number aberrations from individual cases served as input into the CBM (generated from PubMed and other online resources) to create a patient-specific protein network map. Disease-biomarkers unique to each patient were identified within protein network maps. Digital drug simulations were conducted by measuring effect of PC on a cell growth score comprised of a composite of cell proliferation, apoptosis, and other cancer hallmarks. Drug simulations were systematically conducted to identify and evaluate therapeutic efficacy. The drug combination was mapped to the patient genome along with a rational mechanism of action and validated based on the genomic profile and its biological consequences. Results: Of the 22 patients treated with PC, 13 had clinical responses and 9 were non-responders. The computer simulation correctly predicted response in 16/22 with 72.73% accuracy, 55.56% specificity and 84.62% sensitivity. CBM identified novel amplified segments of Chromosome 11p and 1p were responsible for non-responsiveness to PC. Key genes on these chromosomes were identified belonging to the autophagy, reactive oxygen species (ROS) scavenging, DNA repair, and microtubule polymerization pathways. Amplification of AMBRA1, ATG13 and TRAF6 (11p) led to autophagy upregulation resulting in low ROS level, a well-documented resistance loop for chemotherapy. SIRT3 and CAT (11p), ROS scavenging genes, were also upregulated due to increase in copy number. CTH (1p) is another key enzyme involved in GSH-mediated ROS scavenging and was also upregulated. Biosimulation indicated a low ROS level was the key reason of resistance to PC. Heightened DNA damage repair due FANCF and ZNF143 (11p amp) and USP1 (1p amp), was another cause of PC resistance. These discoveries suggest that a combination of an autophagy inhibitor / BCL2 mimetic might prove useful to reverse PC resistance associated with 11p and 1p amp. Conclusions: This study highlights how CBM simulation platform can help to identify novel patient segments for therapy response prediction and use drug re-purposing to overcome chemotherapy resistance.
Abstract
BACKGROUND
TMZ-induced G:T mismatches trigger MMR to perform futile repair of O6-methylguanine leading to apoptosis. Without MMR, the G:T mutation is genomically incorporated to produce DNA ...mutation signature #11. Hypermutation ensues and may compromise survival without the redemptive benefit of immunotherapy. MMR genes are infrequently mutated or deleted. More commonly, MMRD results from epigenetic silencing, transcription failure, or micro-RNA compromising translation.
METHODS
Comprehensive genomic profiling and Cellworks biosimulation was utilized to diagnose MMRD and correlated with survival in 38 TCGA patients with newly diagnosed, IDH wildtype, m-MGMT GBM treated with adjuvant TMZ. The signaling pathway consequences for MutSα and MutLα were assessed. Kaplan-Meier curves were constructed for PFS and OS.
RESULTS
Patients were characterized: MMR proficient (Grp 1) and deficient (Grp 2). Half (19/38) had compromise of 1-10 pathways impacting MMR: 3 had deletions of MLH1 or PMS2. Others included deletions of EP300, CREBBP, KMT2A-D, ARID1A, HUS, or EXO1 and amplifications of KDM4A/C or MIR21/155. Grp 1 had significantly higher MMR biosimulation scores than Grp 2. (p=0.00082). The median PFS was 10.51 and 3.58 months (p=0.0072) and median OS was 16.96 and 9.40 (p=0.0059) months in Group 1&2, respectively.
CONCLUSIONS
Up to half of GBM patients have MMRD caused by pathway dysregulation. Biosimulation of MMRD predicts early progression on TMZ, echoing the long-held observation that TMZ does not trigger apoptosis in MMRD cancers. The study also reports inferior OS for MMRD compared to the historical experience of unmethylated-MGMT patients, suggesting that TMZ-induced hypermutation may compromise survival. As lomustine does not rely on intact MMR, 2nd-line lomustine may have blunted the impact of MMR on OS. Alternatively, upfront lomustine might have produced superior disease control in MMRD patients. Computational biosimulation offers the opportunity to diagnose patients who should not receive TMZ despite m-MGMT and who could benefit from alternative adjuvant strategies.
Background: AML is a heterogeneous hematological cancer, characterized by the clonal expansion of myeloid blasts in the peripheral blood, bone marrow and other tissues. Among adults, AML is the ...leading cause of leukemia-associated death. Patients are often elderly and have comorbid conditions. Response to remission induction therapy varies by biologic subtype and by the drugs used for induction, but responses to each therapy are not predictable, even within specific biologic subgroups. To improve prognostication and identify new therapeutic options, Cellworks developed the Cellworks Omics Biology Model (CBM) that relies on multi-omics inputs from malignant cells to predict response and identify potential therapies tailored to the unique mechanisms of each patient's disease.
Aim: To predict the response to induction chemotherapy regimens in AML patients and identify predictive genomic signatures, pathways, and personalized treatment options for refractory patients.
Method: 57 AML patients with known therapy response were selected from 8 PubMed publications (Table 1). The cohort was split into 3 groups with CBFB-MYH11 fusion (n=19), RUNX1-RUNX1T1 fusion (n=10) and CEBPα del (n=28). All data was anonymized, de-identified and exempt from IRB review. NCCN offers specific recommendations for AML patients with these mutations and they are associated with high rates of remission after induction therapy. The available genomic data for each profile was processed using Cellworks’ CBM to generate an AML subtype-specific protein network map using published information from PubMed and other online resources permitting patient-specific biomarkers to be mapped to sensitivity and resistance pathways. Drugs selected from a digital drug library were simulated for each patient across the three cohorts. Benefit was assessed by measuring each drug’s effect on a cell growth score, a composite of proliferation, viability, and apoptosis indices.
Results: Of 57 patients with favorable-risk AML, 6 (11%) experienced induction failure. 1/19 patients harboring CBFB-MYH11 fusion failed to respond to induction therapy (Table 2) and CBM identified co-occurring gene aberrations responsible for resistance in the patient with resistant disease (ID:757637), who harbored trisomy 6 with amplification of GSTA1, GSTA2, GSTA4, DEK, TFAP2A, NFYA and EHMT2 causing dysregulation of pathways involved in treatment failure. CBM also identified 3 prospective therapies that target this patient's specific biomarkers (Table 3).
Similarly, 2/10 patients with RUNX1-RUNX1T1 fusion failed to respond to induction therapy. CBM identified a loss of function mutation in EZH2 causing increased levels of HOXA5 and HOXA9 as key orchestrators of treatment failure (Patient ID: 757805). Furthermore, CBM also identified 3 prospective therapies for this patient, targeting patient-specific resistance mechanisms (Table 3).
In the CEBPα cohort, 3/28 patients did not respond to induction therapy (Table 2). For Patient ID:755125, CBM identified a DNMT3A loss of function mutation and consequential gain in the levels of HOXA5 and HOXA9 as key orchestrators of treatment failure. Again, CBM identified 3 novel therapies for this patient targeting patient-specific biomarkers (Table 3).
Conclusion: Even in biological subgroups of AML expected to have sensitive disease, induction failure is not rare. Fortunately, the CBM could identify causes of failure and suggest alternative therapies based on co-occurring genomic abnormalities to mitigate the ineffectiveness of standard induction regimens in patients with resistant disease despite their favorable biology. The identification of patient-specific resistance mechanisms characterizes a new therapeutic imperative founded on deep molecular diagnosis that promises to enhance disease outcomes, inform treatment planning, avoid adverse events from ineffective therapies, and reduce costs. Also, computational modeling identifies alternate therapy regimens for refractory patients by incorporating patient-specific disease biomarkers. For all the non-responders, CBM identified 3 potential therapies across each fusion subgroup. Although these combinations need further validation in the clinic, the Cellworks CBM biosimulation platform allows for precision medicine approaches that target patient-specific disease biomarkers and treatment resistance mechanisms.
Display omitted
Castro:Cellworks Group Inc: Consultancy. Nair:Cellworks Research India Private Limited: Current Employment. Grover:Cellworks Research India Private Limited: Current Employment. Kumar:Cellworks Research India Private Limited: Current Employment. Agarwal:Cellworks Research India Private Limited: Current Employment. Suseela:Cellworks Research India Private Limited: Current Employment. Gopi:Cellworks Research India Private Limited: Current Employment. Ganesh:Cellworks Research India Private Limited: Current Employment. Shyamasundar:Cellworks Research India Private Limited: Current Employment. Kulkarni:Cellworks Research India Private Limited: Current Employment. Sauban:Cellworks Research India Private Limited: Current Employment. Balla:Cellworks Research India Private Limited: Current Employment. Chafekar:Cellworks Research India Private Limited: Current Employment. Choudhury:Cellworks Research India Private Limited: Current Employment. Mundkur:Cellworks Group Inc.: Current Employment. Macpherson:Cellworks Group Inc.: Current Employment. Kapoor:Cellworks Research India Private Limited: Current Employment. Howard:Boston Scientific: Consultancy; Cellworks: Consultancy; EUSA Pharma: Consultancy; Sanofi: Consultancy, Other: Speaker; Servier: Consultancy, Other: Speaker.
PDF
