Machine Learning Predicts Oxaliplatin Benefit in Early Colon Cancer.

PURPOSE: A combination of fluorouracil, leucovorin, and oxaliplatin (FOLFOX) is the standard for adjuvant therapy of resected early-stage colon cancer (CC). Oxaliplatin leads to lasting and disabling neurotoxicity. Reserving the regimen for patients who benefit from oxaliplatin would maximize efficacy and minimize unnecessary adverse side effects. METHODS: We trained a new machine learning model, referred to as the colon oxaliplatin signature (COLOXIS) model, for predicting response to oxaliplatin-containing regimens. We examined whether COLOXIS was predictive of oxaliplatin benefits in the CC adjuvant setting among 1,065 patients treated with 5-fluorouracil plus leucovorin (FULV; n = 421) or FULV + oxaliplatin (FOLFOX; n = 644) from NSABP C-07 and C-08 phase III trials. The COLOXIS model dichotomizes patients into COLOXIS+ (oxaliplatin responder) and COLOXIS- (nonresponder) groups. Eight-year recurrence-free survival was used to evaluate oxaliplatin benefits within each of the groups, and the predictive value of the COLOXIS model was assessed using the P value associated with the interaction term (int P) between the model prediction and the treatment effect. RESULTS: Among 1,065 patients, 526 were predicted as COLOXIS+ and 539 as COLOXIS-. The COLOXIS+ prediction was associated with prognosis for FULV-treated patients (hazard ratio [HR], 1.52 [95% CI, 1.07 to 2.15]; P = .017). The model was predictive of oxaliplatin benefits: COLOXIS+ patients benefited from oxaliplatin (HR, 0.65 [95% CI, 0.48 to 0.89]; P = .0065; int P = .03), but COLOXIS- patients did not (COLOXIS- HR, 1.08 [95% CI, 0.77 to 1.52]; P = .65). CONCLUSION: The COLOXIS model is predictive of oxaliplatin benefits in the CC adjuvant setting. The results provide evidence supporting a change in CC adjuvant therapy: reserve oxaliplatin only for COLOXIS+ patients, but further investigation is warranted.

Breast Cancer Index and Prediction of Extended Aromatase Inhibitor Therapy Benefit in Hormone Receptor-Positive Breast Cancer from the NRG Oncology/NSABP B-42 Trial.

PURPOSE: BCI (H/I) has been shown to predict extended endocrine therapy (EET) benefit. We examined BCI (H/I) for EET benefit prediction in NSABP B-42, which evaluated extended letrozole therapy (ELT) in patients with hormone receptor-positive breast cancer after 5 years of ET. EXPERIMENTAL DESIGN: A stratified Cox model was used to analyze RFI as the primary endpoint, with DR, BCFI, and DFS as secondary endpoints. Because of a nonproportional effect of ELT on DR, time-dependent analyses were performed. RESULTS: The translational cohort included 2,178 patients (45% BCI (H/I)-High, 55% BCI (H/I)-Low). ELT showed an absolute 10-year RFI benefit of 1.6% (P = 0.10), resulting in an underpowered primary analysis (50% power). ELT benefit and BCI (H/I) did not show a significant interaction for RFI (BCI (H/I)-Low: 10 years absolute benefit 1.1% [HR, 0.70; 95% confidence interval (CI), 0.43-1.12; P = 0.13]; BCI (H/I)-High: 2.4% [HR, 0.83; 95% CI, 0.55-1.26; P = 0.38]; Pinteraction = 0.56). Time-dependent DR analysis showed that after 4 years, BCI (H/I)-High patients had significant ELT benefit (HR = 0.29; 95% CI, 0.12-0.69; P < 0.01), whereas BCI (H/I)-Low patients were less likely to benefit (HR, 0.68; 95% CI, 0.33-1.39; P = 0.29; Pinteraction = 0.14). Prediction of ELT benefit by BCI (H/I) was more apparent in the HER2- subset after 4 years (ELT-by-BCI (H/I) Pinteraction = 0.04). CONCLUSIONS: BCI (H/I)-High versus BCI (H/I)-Low did not show a statistically significant difference in ELT benefit for the primary endpoint (RFI). However, in time-dependent DR analysis, BCI (H/I)-High patients experienced statistically significant benefit from ELT after 4 years, whereas (H/I)-Low patients did not. Because BCI (H/I) has been validated as a predictive marker of EET benefit in other trials, additional follow-up may enable further characterization of BCI's predictive ability.

DEEPOMICS FFPE, a deep neural network model, identifies DNA sequencing artifacts from formalin fixed paraffin embedded tissue with high accuracy.

Formalin-fixed, paraffin-embedded (FFPE) tissue specimens are routinely used in pathological diagnosis, but their large number of artifactual mutations complicate the evaluation of companion diagnostics and analysis of next-generation sequencing data. Identification of variants with low allele frequencies is challenging because existing FFPE filtering tools label all low-frequency variants as artifacts. To address this problem, we aimed to develop DEEPOMICS FFPE, an AI model that can classify a true variant from an artifact. Paired whole exome sequencing data from fresh frozen and FFPE samples from 24 tumors were obtained from public sources and used as training and validation sets at a ratio of 7:3. A deep neural network model with three hidden layers was trained with input features using outputs of the MuTect2 caller. Contributing features were identified using the SHapley Additive exPlanations algorithm and optimized based on training results. The performance of the final model (DEEPOMICS FFPE) was compared with those of existing models (MuTect filter, FFPolish, and SOBDetector) by using well-defined test datasets. We found 41 discriminating properties for FFPE artifacts. Optimization of property quantification improved the model performance. DEEPOMICS FFPE removed 99.6% of artifacts while maintaining 87.1% of true variants, with an F1-score of 88.3 in the entire dataset not used for training, which is significantly higher than those of existing tools. Its performance was maintained even for low-allele-fraction variants with a specificity of 0.995, suggesting that it can be used to identify subclonal variants. Different from existing methods, DEEPOMICS FFPE identified most of the sequencing artifacts in the FFPE samples while retaining more of true variants, including those of low allele frequencies. The newly developed tool DEEPOMICS FFPE may be useful in designing capture panels for personalized circulating tumor DNA assay and identifying candidate neoepitopes for personalized vaccine design. DEEPOMICS FFPE is freely available on the web ( http://deepomics.co.kr/ffpe ) for research.

Gene Expression and Benefit of Chemotherapy in Women With Node-Negative, Estrogen Receptor-Positive Breast Cancer.

PURPOSE: The 21-gene recurrence score (RS) assay quantifies the likelihood of distant recurrence in women with estrogen receptor-positive, lymph node-negative breast cancer treated with adjuvant tamoxifen. The relationship between the RS and chemotherapy benefit is not known. METHODS: The RS was measured in tumors from the tamoxifen-treated and tamoxifen plus chemotherapy-treated patients in the National Surgical Adjuvant Breast and Bowel Project (NSABP) B20 trial. Cox proportional hazards models were utilized to test for interaction between chemotherapy treatment and the RS. RESULTS: A total of 651 patients were assessable (227 randomly assigned to tamoxifen and 424 randomly assigned to tamoxifen plus chemotherapy). The test for interaction between chemotherapy treatment and RS was statistically significant (P = .038). Patients with high-RS (≥ 31) tumors (ie, high risk of recurrence) had a large benefit from chemotherapy (relative risk, 0.26; 95% CI, 0.13 to 0.53; absolute decrease in 10-year distant recurrence rate: mean, 27.6%; SE, 8.0%). Patients with low-RS (< 18) tumors derived minimal, if any, benefit from chemotherapy treatment (relative risk, 1.31; 95% CI, 0.46 to 3.78; absolute decrease in distant recurrence rate at 10 years: mean, -1.1%; SE, 2.2%). Patients with intermediate-RS tumors did not appear to have a large benefit, but the uncertainty in the estimate can not exclude a clinically important benefit. CONCLUSION: The RS assay not only quantifies the likelihood of breast cancer recurrence in women with node-negative, estrogen receptor-positive breast cancer, but also predicts the magnitude of chemotherapy benefit.

Replication of genetic associations of chemotherapy-related cardiotoxicity in the adjuvant NSABP B-31 clinical trial.

Background: The cardiotoxic effects of doxorubicin, trastuzumab, and other anticancer agents are well known, but molecular genetic testing is lacking for the early identification of patients at risk for therapy-related cardiac toxicity. Methods: Using the Agena Bioscience MassARRAY system, we genotyped TRPC6 rs77679196, BRINP1 rs62568637, LDB2 rs55756123, RAB22A rs707557, intergenic rs4305714, LINC01060 rs7698718, and CBR3 rs1056892 (V244M) (previously associated with either doxorubicin or trastuzumab-related cardiotoxicity in the NCCTG N9831 trial of anthracycline-based chemotherapy ± trastuzumab) in 993 patients with HER2+ early breast cancer from the NSABP B-31 trial of adjuvant anthracycline-based chemotherapy ± trastuzumab. Association analyses were performed with outcomes of congestive heart failure (N = 29) and maximum decline in left ventricular ejection fraction (LVEF) using logistic and linear regression models, respectively, under an additive model with age, baseline LVEF, and previous use of hypertensive medications as covariates. Results: Associations of maximum decline in LVEF in the NCCTG N9831 patients did not replicate in the NSABP B-31 patients. However, TRPC6 rs77679196 and CBR3 rs1056892 were significantly associated with congestive heart failure, p < 0.05, with stronger associations observed in patients treated with chemotherapy only (no trastuzumab) or in the combined analysis of all patients relative to those patients treated with chemotherapy + trastuzumab. Conclusions: TRPC6 rs77679196 and CBR3 rs1056892 (V244M) are associated with doxorubicin-induced cardiac events in both NCCTG N9831 and NSABP B-31. Other variants previously associated with trastuzumab-related decline in LVEF failed to replicate between these studies.

Copy number aberrations in circulating tumor DNA enables prognosis prediction and molecular characterization of breast cancer.

BACKGROUND: Low-pass whole-genome sequencing (LP-WGS)-based circulating tumor DNA (ctDNA) analysis is a versatile tool for somatic copy number aberration (CNA) detection, and this study aims to explore its clinical implication in breast cancer. METHODS: We analyzed LP-WGS ctDNA data from 207 metastatic breast cancer (MBC) patients to explore prognostic value of ctDNA CNA burden and validated it in 465 stage II-III triple-negative breast cancer (TNBC) patients who received neoadjuvant chemotherapy in phase III PEARLY trial (NCT02441933). The clinical implication of locus level LP-WGS ctDNA profiling was further evaluated. RESULTS: We found that a high baseline ctDNA CNA burden predicts poor overall survival and progression-free survival of MBC patients. The post hoc analysis of the PEARLY trial showed that a high baseline ctDNA CNA burden predicted poor disease-free survival independent from pathologic complete response (pCR), validating its robust prognostic significance. The 24-month disease-free survival rate was 96.9% and 55.9% in [pCR(+) and low I-score] and [non-pCR and high I-score] patients, respectively. The locus-level ctDNA CNA profile classified MBC patients into 5 molecular clusters and revealed targetable oncogenic CNAs. LP-WGS ctDNA and in vitro analysis identified the BCL6 amplification as a resistance factor for CDK4/6 inhibitors. We estimated ctDNA-based homologous recombination deficiency status of patients by shallowHRD algorithm, which was highest in the TNBC and correlated with platinum-based chemotherapy response. CONCLUSIONS: These results demonstrate LP-WGS ctDNA CNA analysis as an essential tool for prognosis prediction and molecular profiling. Particularly, ctDNA CNA burden can serve as a useful determinant for escalating or de-escalating (neo)adjuvant strategy in TNBC patients.

Fusobacterium nucleatum induces a tumor microenvironment with diminished adaptive immunity against colorectal cancers.

Background & Aims: Fusobacterium nucleatum (FN) plays a pivotal role in the development and progression of colorectal cancer by modulating antitumor immune responses. However, the impact of FN on immune regulation in the tumor microenvironment has not been fully elucidated. Methods: The abundance of FN was measured in 99 stage III CRC tumor tissues using quantitative polymerase chain reaction. Gene expression profiles were assessed and annotated using consensus molecular subtypes (CMS), Gene Ontology (GO) analysis, and deconvolution of individual immune cell types in the context of FN abundance. Immune profiling for tumor infiltrating T cells isolated from human tumor tissues was analyzed using flow cytometry. Ex vivo tumor-infiltrating T cells were stimulated in the presence or absence of FN to determine the direct effects of FN on immune cell phenotypes. Results: Gene expression profiles, CMS composition, abundance of immune cell subtypes, and survival outcomes differed depending on FN infection. We found that FN infection was associated with poorer disease-free survival and overall survival in stage III CRC patients. FN infection was associated with T cell depletion and enrichment of exhausted CD8+ and FoxP3+ regulatory T cells in the tumor microenvironment. The presence of FN in tumors was correlated with a suppressive tumor microenvironment in a T cell-dependent manner. Conclusion: FN enhanced the suppressive immune microenvironment with high depletion of CD8+ T cells and enrichment of FoxP3+ regulatory T cells in human colorectal cancer cases. Our findings suggest a potential association for FN in adaptive immunity, with biological and prognostic implications.

Long-term outcomes of dual vs single HER2-directed neoadjuvant therapy in NSABP B-41.

BACKGROUND: The primary aim of this randomized neoadjuvant trial in operable, HER2-positive breast cancer, was to determine the efficacy on pathologic complete response (pCR) of substituting lapatinib (L) for trastuzumab (T) or adding L to T, in combination with weekly paclitaxel (WP) following AC. Results on pCR were previously reported. Here, we report data on planned secondary endpoints, recurrence-free interval (RFI) post-surgery, and overall survival (OS). METHODS: All patients received standard AC q3 weeks × 4 cycles followed by WP (80 mg/m2) on days 1, 8, and 15, q28 days × 4 cycles. Concurrently with WP, patients received either T (4 mg/kg load, then 2 mg/kg) weekly until surgery, L (1250 mg) daily until surgery, or weekly T plus L (750 mg) daily until surgery. Following surgery, all patients received T to complete 52 weeks of HER2-targeted therapy. 522 of 529 randomized patients had follow-up. Median follow-up was 5.1 years. RESULTS: RFI at 4.5 years was 87.2%, 79.4% (p = 0.34; HR = 1.37; 95% CI 0.80, 2.34), and 89.4% (p = 0.37; HR = 0.70; 0.37, 1.32) for arms T, L, and TL, respectively. The corresponding five-year OS was 94.8%, 89.1% (p = 0.34; HR = 1.46; 0.68, 3.11), and 95.8% (p = 0.25; HR = 0.58; 0.22, 1.51), respectively. Patients with pCR had a much better prognosis, especially in the ER-negative cohort: RFI (HR = 0.23, p < 0.001) and OS (HR = 0.28, p < 0.001). CONCLUSIONS: Although pCR, RFI, and OS were numerically better with the dual combination and less with L, the differences were not statistically significant. However, achievement of pCR again correlated with improved outcomes, especially remarkable in the ER-negative subset. CLINICAL TRIALS REGISTRATION: NCT00486668.

Machine Learning-Based Mortality Prediction Model for Critically Ill Cancer Patients Admitted to the Intensive Care Unit (CanICU).

BACKGROUND: Although cancer patients are increasingly admitted to the intensive care unit (ICU) for cancer- or treatment-related complications, improved mortality prediction remains a big challenge. This study describes a new ML-based mortality prediction model for critically ill cancer patients admitted to ICU. PATIENTS AND METHODS: We developed CanICU, a machine learning-based 28-day mortality prediction model for adult cancer patients admitted to ICU from Medical Information Mart for Intensive Care (MIMIC) database in the USA (n = 766), Yonsei Cancer Center (YCC, n = 3571), and Samsung Medical Center in Korea (SMC, n = 2563) from 2 January 2008 to 31 December 2017. The accuracy of CanICU was measured using sensitivity, specificity, and area under the receiver operating curve (AUROC). RESULTS: A total of 6900 patients were included, with a 28-day mortality of 10.2%/12.7%/36.6% and a 1-year mortality of 30.0%/36.6%/58.5% in the YCC, SMC, and MIMIC-III cohort. Nine clinical and laboratory factors were used to construct the classifier using a random forest machine-learning algorithm. CanICU had 96% sensitivity/73% specificity with the area under the receiver operating characteristic (AUROC) of 0.94 for 28-day, showing better performance than current prognostic models, including the Acute Physiology and Chronic Health Evaluation (APACHE) or Sequential Organ Failure Assessment (SOFA) score. Application of CanICU in two external data sets across the countries yielded 79-89% sensitivity, 58-59% specificity, and 0.75-0.78 AUROC for 28-day mortality. The CanICU score was also correlated with one-year mortality with 88-93% specificity. CONCLUSION: CanICU offers improved performance for predicting mortality in critically ill cancer patients admitted to ICU. A user-friendly online implementation is available and should be valuable for better mortality risk stratification to allocate ICU care for cancer patients.

Association between Fusobacterium nucleatum and patient prognosis in metastatic colon cancer.

Recent evidence suggests that Fusobacterium nucleatum (Fn) is associated with the development and progression of colorectal cancer. We aimed to delineate the clinical implications of Fn in metastatic colon cancer. We performed quantitative polymerase chain reaction (qPCR) using DNA samples from synchronous metastatic colon cancer patients with either formalin-fixed paraffin-embedded (FFPE) archival primary site tumor samples or fresh colon tissues. Progression-free survival (PFS)1 and PFS2 were defined as PFS of first- and second-line palliative settings. qPCR for Fn was successfully performed using 112 samples (FFPE, n = 61; fresh tissue, n = 51). Forty-one and 68 patients had right-sided and left-sided colon cancer, respectively. Patients with Fn enriched right-sided colon cancers had shorter PFS1 (9.7 vs. 11.2 months) than the other subgroups (HR 3.54, 95% confidence interval [CI] 1.05-11.99; P = 0.04). Fn positive right-sided colon was also associated with shorter PFS2 (3.7 vs. 6.7 months; HR 2.34, 95% CI 0.69-7.91; P = 0.04). In the univariate analysis, PFS1 was affected by differentiation and Fn positive right-sided colon cancer. The multivariate analysis showed that differentiation (HR 2.68, 95% CI 1.40-5.14, P = 0.01) and Fn positive right-sided colon (HR 0.40, 95% CI 0.18-0.88, P = 0.02) were associated with PFS1. Fn enrichment in right sided colon was not associated with overall survival (OS). Fn enrichment has significantly worse prognosis in terms of PFS1 and PFS2 in patients with right-sided metastatic colon cancers.

Genomic landscape of extraordinary responses in metastatic breast cancer.

Extreme responders to anticancer therapy are rare among advanced breast cancer patients. Researchers, however, have yet to investigate treatment responses therein on the whole exome level. We performed whole exome analysis to characterize the genomic landscape of extreme responders among metastatic breast cancer patients. Clinical samples were obtained from breast cancer patients who showed exceptional responses to anti-HER2 therapy or hormonal therapy and from those who did not. Matched breast tumor tissue (somatic DNA) and blood samples (germline DNA) were collected from a total of 30 responders and 15 non-responders. Whole exome sequencing using Illumina HiSeq2500 was performed for all 45 patients (90 samples). Somatic single nucleotide variants (SNVs), indels, and copy number variants (CNVs) were identified for the genomes of each patient. Group-specific somatic variants and mutational burden were statistically analyzed. Sequencing of cancer exomes for all patients revealed 1839 somatic SNVs (1661 missense, 120 nonsense, 43 splice-site, 15 start/stop-lost) and 368 insertions/deletions (273 frameshift, 95 in-frame), with a median of 0.7 mutations per megabase (range, 0.08 to 4.2 mutations per megabase). Responders harbored a significantly lower nonsynonymous mutational burden (median, 26 vs. 59, P = 0.02) and fewer CNVs (median 13.6 vs. 97.7, P = 0.05) than non-responders. Multivariate analyses of factors influencing progression-free survival showed that a high mutational burden and visceral metastases were significantly related with disease progression. Extreme responders to treatment for metastatic breast cancer are characterized by fewer nonsynonymous mutations and CNVs.

Systematic evaluation of scoring methods for Ki67 as a surrogate for 21-gene recurrence score.

Although Ki67 labeling index is a potential predictive marker for chemotherapy benefit, its clinical utility has been limited by the lack of a standard scoring method resulting in poor interobserver reproducibility. Especially, there is no consensus on the use of average versus hotspot score for reporting. In order to determine the best method for Ki67 scoring and validate manual scoring method proposed by the International Ki67 Working Group (IKWG), we systematically compared average versus hotspot score in 240 cases with a public domain image analysis program QuPath. We used OncotypeDx Recurrence Score (RS) as a benchmark to compare the potential clinical utility of each scoring methods. Both average and hotspot scores showed statistically significant but only modest correlation with OncotypeDx RS. Only hotspot score could meaningfully distinguish RS low-risk versus high-risk patients. However, hotspot score was less reproducible limiting its clinical utility. In summary, our data demonstrate that utility of the Ki67 labeling index is influenced by the choice of scoring method.

Dynamic changes in circulating PD-1+CD8+ T lymphocytes for predicting treatment response to PD-1 blockade in patients with non-small-cell lung cancer.

BACKGROUND: The predictive value of immune monitoring with circulating CD8+ T lymphocytes for treatment response to programmed cell death protein 1 (PD-1) inhibitors has not been explored in non-small-cell lung cancer (NSCLC), prompting us to investigate whether dynamic changes in PD-1+CD8+ T lymphocytes have predictive value for durable clinical benefit (DCB) and survival after PD-1 blockade. METHODS: Patients with recurrent and/or metastatic NSCLC treated with PD-1 inhibitors were enrolled (discovery cohort; n = 94). Peripheral blood was obtained immediately before and after one cycle of treatment with PD-1 blockade. Phenotyping of circulating CD8+ T lymphocytes was conducted using multi-colour flow cytometry. Predictive values of dynamic changes in circulating PD-1+CD8+ T lymphocytes during the first cycle were validated in an independent cohort (validation cohort; n = 54) of a prospective trial with a PD-1 inhibitor (NCT03486119). RESULTS: Circulating PD-1+CD8+ T lymphocytes were enriched with effector/memory populations with elevated expression of activation- and exhaustion-related markers. Reduction in the frequency of PD-1+ cells among CD8+ T lymphocytes after one cycle of treatment was associated with a higher probability of DCB and superior survival outcomes in the discovery cohort. Similar results were obtained in the analysis of tumour antigen NY-ESO-1-specific CD8+ T lymphocytes and the validation cohort. Mechanistically, PD-1 molecule expression on CD8+ T lymphocytes suppresses the effector functions of tumour antigen-specific CD8+ T lymphocytes. CONCLUSIONS: Dynamic changes in circulating PD-1+CD8+ T lymphocytes predict clinical, and survival benefit from PD-1 blockade treatment in NSCLC, providing a useful tool to identify patient subgroups who will optimally benefit from PD-1 inhibitors.

An Improved, Assay Platform Agnostic, Absolute Single Sample Breast Cancer Subtype Classifier.

While intrinsic molecular subtypes provide important biological classification of breast cancer, the subtype assignment of individuals is influenced by assay technology and study cohort composition. We sought to develop a platform-independent absolute single-sample subtype classifier based on a minimal number of genes. Pairwise ratios for subtype-specific differentially expressed genes from un-normalized expression data from 432 breast cancer (BC) samples of The Cancer Genome Atlas (TCGA) were used as inputs for machine learning. The subtype classifier with the fewest number of genes and maximal classification power was selected during cross-validation. The final model was evaluated on 5816 samples from 10 independent studies profiled with four different assay platforms. Upon cross-validation within the TCGA cohort, a random forest classifier (MiniABS) with 11 genes achieved the best accuracy of 88.2%. Applying MiniABS to five validation sets of RNA-seq and microarray data showed an average accuracy of 85.15% (vs. 77.72% for Absolute Intrinsic Molecular Subtype (AIMS)). Only MiniABS could be applied to five low-throughput datasets, showing an average accuracy of 87.93%. The MiniABS can absolutely subtype BC using the raw expression levels of only 11 genes, regardless of assay platform, with higher accuracy than existing methods.

Validation of the NSABP/NRG Oncology 8-Gene Trastuzumab-benefit Signature in Alliance/NCCTG N9831.

Our objective was to validate the NSABP 8-gene trastuzumab-benefit signature, developed and initially validated in NRG Oncology/NSABP B-31 in Alliance/NCCTG N9831. The B-31 and N9831 trials demonstrated the benefit of adding trastuzumab to chemotherapy in the adjuvant setting for HER2+ breast cancer patients. NSABP investigators utilized gene expression profiles of N9831 patients (N = 892) to blindly assign patients to large-, moderate-, or no-trastuzumab benefit groups and then NCCTG investigators assessed the degree of trastuzumab benefit using Cox models adjusted for age, nodes, estrogen receptor/progesterone receptor status, tumor size, and grade. Hazard ratios and 2-sided P values for recurrence-free survival of the predicted large- (n = 387), moderate- (n = 401), and no-benefit (n = 104) groups, based on the 8-gene signature were 0.47 (95% CI = 0.31 to 0.73, P < .001), 0.60 (95% CI = 0.39 to 0.92, P = .02), and 1.54 (95% CI = 0.59 to 4.02, P = .38), respectively (P interaction  = .02), providing validation of the 8-gene signature in an independent study.

Mouse-human co-clinical trials demonstrate superior anti-tumour effects of buparlisib (BKM120) and cetuximab combination in squamous cell carcinoma of head and neck.

BACKGROUND: Recurrent and/or metastatic squamous cell carcinoma of head and neck (R/M SCCHN) is a common cancer with high recurrence and mortality. Current treatments have low response rates (RRs). METHODS: Fifty-three patients with R/M SCCHN received continuous oral buparlisib. In parallel, patient-derived xenografts (PDXs) were established in mice to evaluate resistance mechanisms and efficacy of buparlisib/cetuximab combination. Baseline and on-treatment tumour genomes and transcriptomes were sequenced. Based on the integrated clinical and PDX data, 11 patients with progression under buparlisib monotherapy were treated with a combination of buparlisib and cetuximab. RESULTS: For buparlisib monotherapy, disease control rate (DCR) was 49%, RR was 3% and median progression-free survival (PFS) and overall survival (OS) were 63 and 143 days, respectively. For combination therapy, DCR was 91%, RR was 18% and median PFS and OS were 111 and 206 days, respectively. Four PDX models were originated from patients enrolled in the current clinical trial. While buparlisib alone did not inhibit tumour growth, combination therapy achieved tumour inhibition in three of seven PDXs. Genes associated with apoptosis and cell-cycle arrest were expressed at higher levels with combination treatment than with buparlisib or cetuximab alone. CONCLUSIONS: The buparlisib/cetuximab combination has significant promise as a treatment strategy for R/M SCCHN. CLINICAL TRIAL REGISTRATION: NCT01527877.

Earlier-Phased Cancer Immunity Cycle Strongly Influences Cancer Immunity in Operable Never-Smoker Lung Adenocarcinoma.

Exome and transcriptome analyses of clinically homogeneous early-stage never-smoker female patients with lung adenocarcinoma were performed to understand tumor-T cell interactions and immune escape points. Using our novel gene panels of eight functional categories in the cancer-immunity cycle, three distinct subgroups were identified in this immune checkpoint blockade-refractory cohort by defective gene expression in two major domains, i.e., type I interferon production/signaling pathway and antigen-presenting machinery. Our approach could play a critical role in understanding immune evasion mechanisms, developing a method for effective selection of rare immune checkpoint blockade responders, and finding new treatment strategies.

Peripheral natural killer cells and myeloid-derived suppressor cells correlate with anti-PD-1 responses in non-small cell lung cancer.

Inhibition of immune checkpoint proteins like programmed death 1 (PD-1) is a promising therapeutic approach for several cancers, including non-small cell lung cancer (NSCLC). Although PD-1 ligand (PD-L1) expression is used to predict anti-PD-1 therapy responses in NSCLC, its accuracy is relatively less. Therefore, we sought to identify a more accurate predictive blood biomarker for evaluating anti-PD-1 response. We evaluated the frequencies of T cells, B cells, natural killer (NK) cells, polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs), mononuclear myeloid-derived suppressor cells (M-MDSCs), and Lox-1+ PMN-MDSCs in peripheral blood samples of 62 NSCLC patients before and after nivolumab treatment. Correlation of immune-cell population frequencies with treatment response, progression-free survival, and overall survival was also determined. After the first treatment, the median NK cell percentage was significantly higher in responders than in non-responders, while the median Lox-1+ PMN-MDSC percentage showed the opposite trend. NK cell frequencies significantly increased in responders but not in non-responders. NK cell frequency inversely correlated with that of Lox-1+ PMN-MDSCs after the first treatment cycle. The NK cell-to-Lox-1+ PMN-MDSC ratio (NMR) was significantly higher in responders than in non-responders. Patients with NMRs ≥ 5.75 after the first cycle had significantly higher objective response rates and longer progression-free and overall survival than those with NMRs <5.75. NMR shows promise as an early predictor of response to further anti-PD-1 therapy.

Destabilization of ß-catenin and RAS by targeting the Wnt/ß-catenin pathway as a potential treatment for triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is a severe and heterogeneous disease that lacks an approved targeted therapy and has a poor clinical outcome to chemotherapy. Although the RAS-ERK signaling axis is rarely mutated in TNBC, ~50% of TNBCs show an increased copy number and overexpression of epidermal growth factor receptor (EGFR). However, EGFR-targeted therapies have offered no improvement in patient survival, underscoring the need to explore downstream targets, including RAS. We found that both ß-catenin and RAS, as well as epidermal growth factor receptor (EGFR), are overexpressed and correlated with one another in tumor tissues of TNBC patients. KYA1797K, an Axin-binding small molecule reducing ß-catenin and RAS expression via degradation and suppressing EGFR expression via transcriptional repression, inhibited the proliferation and the metastatic capability of stable cell lines as well as patient-derived cells (PDCs) established from TNBC patient tissues. KYA1797K also suppressed the stemness of 3D-cultured PDCs and xenografted tumors established by using residual tumors from TNBC patients and those established by the TNBC cell line. Targeting both the Wnt/ß-catenin and RAS-ERK pathways via small molecules simultaneously reducing the levels of ß-catenin, RAS, and EGFR could be a potential therapeutic approach for TNBC.

Establishment and characterization of patient-derived xenografts as paraclinical models for head and neck cancer.

BACKGROUND: We investigated whether head and neck squamous cell carcinoma (HNSCC) patient-derived xenografts (PDXs) reaffirm patient responses to anti-cancer therapeutics. METHODS: Tumors from HNSCC patients were transplanted into immunodeficient mice and propagated via subsequent implantation. We evaluated established PDXs by histology, genomic profiling, and in vivo anti-cancer efficacy testing to confirm them as the authentic in vivo platform. RESULTS: From 62 HNSCCs, 15 (24%) PDXs were established. The primary cancer types were tongue (8), oropharynx (3), hypopharynx (1), ethmoid sinus cancer (1), supraglottic cancer (1), and parotid gland (1); six PDXs (40%) were established from biopsy specimens from advanced HNSCC. PDXs mostly retained donor characteristics and remained stable across passages. PIK3CA (H1047R), HRAS (G12D), and TP53 mutations (H193R, I195T, R248W, R273H, E298X) and EGFR, CCND1, MYC, and PIK3CA amplifications were identified. Using the acquisition method, biopsy showed a significantly higher engraftment rate when compared with that of surgical resection (100% [6/6] vs. 16.1% [9/56], P < 0.001). Specimens obtained from metastatic sites showed a significantly higher engraftment rate than did those from primary sites (100% [9/9] vs. 11.3% [6/53], P < 0.001). Three PDX models from HPV-positive tumors were established, as compared to 12 from HPV-negative (15.8% [3/19] and 27.9% [12/43] respectively, P = 0.311), suggesting that HPV positivity tends to show a low engraftment rate. Drug responses in PDX recapitulated the clinical responses of the matching patients with pan-HER inhibitors and pan-PI3K inhibitor. CONCLUSIONS: Genetically and clinically annotated HNSCC PDXs could be useful preclinical tools for evaluating biomarkers, therapeutic targets, and new drug discovery.