Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

LORIS robustly predicts patient outcomes with immune checkpoint blockade therapy using common clinical, pathologic and genomic features

Abstract

Despite the revolutionary impact of immune checkpoint blockade (ICB) in cancer treatment, accurately predicting patient responses remains challenging. Here, we analyzed a large dataset of 2,881 ICB-treated and 841 non-ICB-treated patients across 18 solid tumor types, encompassing a wide range of clinical, pathologic and genomic features. We developed a clinical score called LORIS (logistic regression-based immunotherapy-response score) using a six-feature logistic regression model. LORIS outperforms previous signatures in predicting ICB response and identifying responsive patients even with low tumor mutational burden or programmed cell death 1 ligand 1 expression. LORIS consistently predicts patient objective response and short-term and long-term survival across most cancer types. Moreover, LORIS showcases a near-monotonic relationship with ICB response probability and patient survival, enabling precise patient stratification. As an accurate, interpretable method using a few readily measurable features, LORIS may help improve clinical decision-making in precision medicine to maximize patient benefit. LORIS is available as an online tool at https://loris.ccr.cancer.gov/.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview of the study.
Fig. 2: Robust prediction of pan-cancer objective response to immunotherapy by a six-variable LLR model.
Fig. 3: LORIS predicts patient outcomes following immunotherapy for both pan-cancer and individual cancer types.
Fig. 4: Monotonic relationship between LORIS and patient objective response probability and survival following immunotherapy.
Fig. 5: LORIS exhibits enhanced predictive efficacy for immunotherapy with respect to its prognostic value in the context of non-ICB treatments.
Fig. 6: Robust prediction of response to immunotherapy in NSCLC with LLR.
Fig. 7: LORIS facilitates more precise ICB response prediction.

Similar content being viewed by others

Data availability

The original data of the Chowell et al. cohort are available in Supplementary Table 3 of ref. 22. The original data of the Shim et al. cohort are available in Supplementary Table 1 of ref. 25. The original data of the Vanguri et al. cohort are available at Synapse (https://www.synapse.org/#!Synapse:syn26642505) and cBioPortal (https://www.cbioportal.org/study/summary?id=lung_msk_mind_2020). The original data of the Kato et al. cohort are available in Supplementary Data 1 of ref. 26. The original data of the Ravi et al. cohort28 are available on Zenodo (https://doi.org/10.5281/zenodo.7625517)54. The original data of the Pradat et al. cohort are available in the Supplementary Tables of ref. 29. Deidentified new data reported in this study for the MSK1 cohort, MSK2 cohort and MSK non-ICB cohort, as well as additional features of participants in the Chowell et al. and Shim et al. cohorts that have not been reported before are included in Supplementary Table 6 and are available online at Zenodo (https://doi.org/10.5281/zenodo.11186449)55. Source data are provided with this paper.

Code availability

All codes that are necessary to reproduce all the results in the paper are implemented in Python and R and are publicly available at GitHub (https://github.com/rootchang/LORIS)56 and Zenodo (https://doi.org/10.5281/zenodo.11186449)55.

References

  1. Topalian, S. L., Taube, J. M., Anders, R. A. & Pardoll, D. M. Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy. Nat. Rev. Cancer 16, 275–287 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Morad, G., Helmink, B. A., Sharma, P. & Wargo, J. A. Hallmarks of response, resistance, and toxicity to immune checkpoint blockade. Cell 184, 5309–5337 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Nishino, M., Ramaiya, N. H., Hatabu, H. & Hodi, F. S. Monitoring immune-checkpoint blockade: response evaluation and biomarker development. Nat. Rev. Clin. Oncol. 14, 655–668 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Goodman, A. M. et al. Tumor mutational burden as an independent predictor of response to immunotherapy in diverse cancers. Mol. Cancer Ther. 16, 2598–2608 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Samstein, R. M. et al. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat. Genet. 51, 202–206 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. McGrail, D. J. et al. High tumor mutation burden fails to predict immune checkpoint blockade response across all cancer types. Ann. Oncol. 32, 661–672 (2021).

    Article  CAS  PubMed  Google Scholar 

  7. Topalian, S. L. et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. New Engl. J. Med. 366, 2443–2454 (2012).

    Article  CAS  PubMed  Google Scholar 

  8. Zhao, P. F., Li, L., Jiang, X. Y. & Li, Q. Mismatch repair deficiency/microsatellite instability-high as a predictor for anti-PD-1/PD-L1 immunotherapy efficacy. J. Hematol. Oncol. 12, 54 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Mandal, R. et al. Genetic diversity of tumors with mismatch repair deficiency influences anti-PD-1 immunotherapy response. Science 364, 485–491 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Le, D. T. et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 357, 409–413 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Chowell, D. et al. Evolutionary divergence of HLA class I genotype impacts efficacy of cancer immunotherapy. Nat. Med. 25, 1715–1720 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Chowell, D. et al. Patient HLA class I genotype influences cancer response to checkpoint blockade immunotherapy. Science 359, 582–587 (2018).

    Article  CAS  PubMed  Google Scholar 

  13. Davoli, T., Uno, H., Wooten, E. C. & Elledge, S. J. Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy. Science 355, eaaf8399 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Chang, T. G. et al. Optimizing cancer immunotherapy response prediction by tumor aneuploidy score and fraction of copy number alterations. npj Precis. Oncol. 7, 54 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Ren, F. P., Zhao, T., Liu, B. & Pan, L. Neutrophil–lymphocyte ratio (NLR) predicted prognosis for advanced non-small-cell lung cancer (NSCLC) patients who received immune checkpoint blockade (ICB). Onco. Targets Ther. 12, 4235–4244 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Valero, C. et al. Pretreatment neutrophil-to-lymphocyte ratio and mutational burden as biomarkers of tumor response to immune checkpoint inhibitors. Nat. Commun. 12, 729 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Yoo, S. K., Chowell, D., Valero, C., Morris, L. G. T. & Chan, T. A. Pre-treatment serum albumin and mutational burden as biomarkers of response to immune checkpoint blockade. npj Precis. Oncol. 6, 23 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Wang, Z. M. et al. Paradoxical effects of obesity on T cell function during tumor progression and PD-1 checkpoint blockade. Nat. Med. 25, 141–151 (2019).

    Article  CAS  PubMed  Google Scholar 

  19. Conforti, F. et al. Cancer immunotherapy efficacy and patients’ sex: a systematic review and meta-analysis. Lancet Oncol. 19, 737–746 (2018).

    Article  CAS  PubMed  Google Scholar 

  20. Kugel, C. H. et al. Age correlates with response to anti-PD1, reflecting age-related differences in intratumoral effector and regulatory T-cell populations. Clin. Cancer Res. 24, 5347–5356 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Litchfield, K. et al. Meta-analysis of tumor- and T cell-intrinsic mechanisms of sensitization to checkpoint inhibition. Cell 184, 596–614 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Chowell, D. et al. Improved prediction of immune checkpoint blockade efficacy across multiple cancer types. Nat. Biotechnol. 40, 499–506 (2022).

    Article  CAS  PubMed  Google Scholar 

  23. Gromeier, M. et al. Very low mutation burden is a feature of inflamed recurrent glioblastomas responsive to cancer immunotherapy. Nat. Commun. 12, 352 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Diggs, L. P. & Hsueh, E. C. Utility of PD-L1 immunohistochemistry assays for predicting PD-1/PD-L1 inhibitor response. Biomark. Res. 5, 12 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Shim, J. H. et al. HLA-corrected tumor mutation burden and homologous recombination deficiency for the prediction of response to PD-(L)1 blockade in advanced non-small-cell lung cancer patients. Ann. Oncol. 31, 902–911 (2020).

    Article  CAS  PubMed  Google Scholar 

  26. Kato, S. et al. Real-world data from a molecular tumor board demonstrates improved outcomes with a precision N-of-One strategy. Nat. Commun. 11, 4965 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Vanguri, R. S. et al. Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer. Nature Cancer 3, 1151–1164 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Ravi, A. et al. Genomic and transcriptomic analysis of checkpoint blockade response in advanced non-small cell lung cancer. Nat. Genet. 55, 807–819 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Pradat, Y. et al. Integrative pan-cancer genomic and transcriptomic analyses of refractory metastatic cancer. Cancer Discov. 13, 1116–1143 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Eisenhauer, E. A. et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur. J. Cancer 45, 228–247 (2009).

    Article  CAS  PubMed  Google Scholar 

  31. Cho, M. S. et al. Platelets increase the expression of PD-L1 in ovarian cancer. Cancers 14, 2498 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Sechidis, K. et al. Distinguishing prognostic and predictive biomarkers: an information theoretic approach. Bioinformatics 34, 3365–3376 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1, 206–215 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Petch, J., Di, S. & Nelson, W. Opening the black box: the promise and limitations of explainable machine learning in cardiology. Can. J. Cardiol. 38, 204–213 (2022).

    Article  PubMed  Google Scholar 

  35. Watson, D. S. et al. Clinical applications of machine learning algorithms: beyond the black box. BMJ 364, l886 (2019).

    Article  PubMed  Google Scholar 

  36. Moons, K. G. M. et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann. Intern. Med. 162, W1–W73 (2015).

    Article  PubMed  Google Scholar 

  37. Sambi, M., Bagheri, L. & Szewczuk, M. R. Current challenges in cancer immunotherapy: multimodal approaches to improve efficacy and patient response rates. J. Oncol. 2019, 4508794 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  38. He, Y. Y. et al. Genomic and transcriptional alterations in first-line chemotherapy exert a potentially unfavorable influence on subsequent immunotherapy in NSCLC. Theranostics 11, 7092–7109 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Haas, L. et al. Acquired resistance to anti-MAPK targeted therapy confers an immune-evasive tumor microenvironment and cross-resistance to immunotherapy in melanoma. Nat. Cancer 2, 693–708 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Auslander, N. et al. Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma. Nat. Med. 24, 1545–1549 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Jiang, P. et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat. Med. 24, 1550–1558 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Bareche, Y. et al. Leveraging big data of immune checkpoint blockade response identifies novel potential targets. Ann. Oncol. 33, 1304–1317 (2022).

    Article  CAS  PubMed  Google Scholar 

  43. Liu, D. et al. Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma. Nat. Med. 25, 1916–1927 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Konečný, J. et al. Federated learning: strategies for improving communication efficiency. Preprint at https://doi.org/10.48550/arXiv.1610.05492 (2016).

  45. Valero, C. et al. The association between tumor mutational burden and prognosis is dependent on treatment context. Nat. Genet. 53, 11–15 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Merino, D. M. et al. Establishing guidelines to harmonize tumor mutational burden (TMB): in silico assessment of variation in TMB quantification across diagnostic platforms: phase I of the Friends of Cancer Research TMB Harmonization Project. J. Immunother. Cancer 8, e000147 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Kim, C. G. et al. On-treatment derived neutrophil-to-lymphocyte ratio and survival with palbociclib and endocrine treatment: analysis of a multicenter retrospective cohort and the PALOMA-2/3 study with immune correlates. Breast Cancer Res. 25, 4 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Proctor, M. J. et al. A derived neutrophil to lymphocyte ratio predicts survival in patients with cancer. Br. J. Cancer 107, 695–699 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Wen, P. Y. et al. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J. Clin. Oncol. 28, 1963–1972 (2010).

    Article  PubMed  Google Scholar 

  50. Chicco, D. & Jurman, G. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21, 6 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Velez, D. R. et al. A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genet. Epidemiol. 31, 306–315 (2007).

    Article  PubMed  Google Scholar 

  52. DeLong, E. R., DeLong, D. M. & Clarke-Pearson, D. L. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44, 837–845 (1988).

    Article  CAS  PubMed  Google Scholar 

  53. Therneau, T. A package for survival analysis in S. CRAN https://CRAN.R-project.org/package=survival (2015).

  54. Holton, M., Arniella, M., Ravi, A. & Getz, G. Genomic and transcriptomic analysis of checkpoint blockade response in advanced non-small cell lung cancer. Zenodo https://doi.org/10.5281/zenodo.7625517 (2023).

  55. Chang, T. LORIS: a logistic regression-based immunotherapy-response score. Zenodo https://doi.org/10.5281/zenodo.11186449 (2024).

  56. Chang, T. et al. LORIS: a logistic regression-based immunotherapy-response score. GitHub https://github.com/rootchang/LORIS (2024).

Download references

Acknowledgements

This research is supported in part by the Intramural Research Program of the National Institutes of Health, National Cancer Institute, Center for Cancer Research. This work used the computational resources of the NIH HPC Biowulf cluster (https://hpc.nih.gov).

Author information

Authors and Affiliations

Authors

Contributions

T.-G.C., E.R. and L.G.T.M conceptualized and designed the study. T.-G.C., Y.C. and S.R.D. developed the machine learning models. T.-G.C., H.J.S., Y.C., S.R.D., S.-H.L., C.V., S.-K.Y., D.C., L.G.T.M. and E.R. acquired, analyzed or interpreted the data. All authors critically revised the manuscript for important intellectual content. E.R. and L.G.T.M. supervised the study.

Corresponding authors

Correspondence to Luc G. T. Morris or Eytan Ruppin.

Ethics declarations

Competing interests

E.R. is a cofounder of MedAware, Metabomed and Pangea Biomed (divested) and an unpaid member of Pangea Biomed’s scientific advisory board. L.G.T.M. is listed as an inventor on intellectual property owned by MSK related to the use of TMB in cancer immunotherapy, unrelated to this work. The other authors declare no competing interests.

Peer review

Peer review information

Nature Cancer thanks Justin Gainor, Hajime Uno and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 An illustration of cohorts used in this study (a-b) and feature importance by the logistic LASSO regression model (c-d).

a. The relationship between cohorts used in this study, the number of participants in each cohort, and the number of participants with complete data for the pan-cancer model and the NSCLC-specific model. The cohorts shaded in light grey represent the training cohorts for the pan-cancer and NSCLC-specific models, respectively. In the figure, ‘n’ represents the number of participants. b. The cancer composition of the non-ICB cohort. Note that three cancer types, mesothelioma, cancer of unknown primary, and central nervous system cancer are not present in this cohort. c-d. Feature importance of from the 8-feature logistic regression classifier using features commonly measured across most participants (c) and feature importance of the final 6-feature logistic regression classifier LLR6 (d). Feature importance is calculated as the absolute values of the corresponding coefficients in the logistic regression models. Importance for cancer type is calculated as the average importance of individual cancer types.

Source data

Extended Data Fig. 2 Comparison between the pan-cancer LLR6 model and the RF16 (Chowell et al.) model.

a. Comparison of the predictive power between the two models on 2,000-repeated 5-fold cross-validation sets using multiple metrics (n = 10,000 repetitions). Error bars, mean ± s.d. P values, two-tailed Mann-Whitney U test. Note that p values are only shown when values for LLR6 (blue bars) are significantly higher than RF16 (Chowell et al.) (green bars). b. Same as panel a, but the metrics represent the difference between those on the training sets and those on the corresponding cross-validation sets (n = 10,000 repetitions). Error bars, mean ± s.d. P values, two-tailed Mann-Whitney U test. c. Receiver operating characteristic curves and corresponding AUCs of LLR6 (blue curves) and RF16 (Chowell et al.) (orange curves) on the training (n = 964 participants) and unseen test (n = 515 participants) sets. Note that while the performance of RF16 (Chowell et al.) is better on the training set, the performance of the much simpler LLR6 model is better on the unseen test set. d. Correlation between the scores from LLR6 and RF16 (Chowell et al.) on both training and unseen test sets, respectively. Spearman correlation coefficients are shown.

Source data

Extended Data Fig. 3 LORIS predicts PFS following immunotherapy for both pan-cancer and individual cancer types.

a. Kaplan–Meier analysis of PFS. TMB is binned at 10 mutations per Mb and LORIS is binned at 0.5. HRs with 95% confidence intervals are shown. P values, univariable Cox proportional hazards regression. H, high; L, low. In the risk table, the numbers represent the number of participants. b. Same as panel a, but TMB is binned at the highest 20th percentile and LORIS is binned at the 50th percentile for each cancer type. HRs with 95% confidence intervals are shown. P values, univariable Cox proportional hazards regression. H, high; L, low. c, d. Forest plot of HRs of PFS within each cancer type using LORIS (binned at the 50th percentile; c) or TMB (binned at the highest 20th percentile; d). P values, multivariable Cox proportional hazards regression with adjustment for cancer type, age, ICB drug class, and year of ICB start. Squares positioned at midpoints symbolize point estimates of HRs, and the accompanying bars indicate 95% confidence intervals. e,f. Comparison of half-year, 1-year, 2-year, 3-year, 4-year, and 5-year PFS stratified by cancer type for high versus low LORIS (binned at the 50th percentile; e) and high versus low TMB (binned at the highest 20th percentile; f). Median survival probability differences (∆) are displayed. P values, two-tailed paired Wilcoxon rank sum test. Box boundaries represent the first and third quartiles; the central line marks the median. Whiskers extend to the furthest non-outlier points within 1.5 times the interquartile range. Data are from combined Chowell test and MSK1 sets (n = 968 participants).

Source data

Extended Data Fig. 4 LORIS has better prediction power of immunotherapy than TMB (a-d) and has enhanced predictive power over prognosis (e).

a-b. Kaplan–Meier analysis of PFS (a) and OS (b). Both TMB and LORIS are binned at the 50th percentile for each cancer type. HRs with 95% confidence intervals are shown. P values, univariable Cox proportional hazards regression. H, high; L, low. Data are from combined Chowell test and MSK1 sets (n = 968 participants). c-d. Kaplan–Meier analysis of LORIS (c) or TMB (d) binned at the different percentiles in each cancer type. P values next to the legend indicate pairwise single-tail comparisons testing against the hypothesis that ‘higher scored participants do not have better survival than lower scored participants’ with univariable Cox proportional hazards regression. HRs with 95% confidence intervals are shown for the lowest-percentile (0–10%) and the highest-percentile groups (90–100%) with univariable Cox proportional hazards regression. Data are from combined Chowell test and MSK1 sets (n = 968 participants). e. Receiver operating characteristic curves and corresponding AUCs with 95% confidence intervals of LORIS on 0.5-year OS, 1-year OS, 2-year OS, and 3-year OS of participants treated with ICB (blue curves) or non-ICB (orange curves) therapies. P values, two-tailed DeLong’s test. ICB data are from combined Chowell test and MSK1 sets (n = 968 participants). Non-ICB data are from the MSK non-ICB cohort (n = 841 participants). The dashed lines represent random performance, serving as a baseline with an AUC of 0.5. This indicates the performance expected from a classifier making random guesses.

Source data

Extended Data Fig. 5 Kaplan–Meier analysis of survival in individual cancer types.

Patients are grouped into LORIS-high (orange curves) and LORIS-low (blue curves) risk groups. LORIS is binned at the 50th percentile for each cancer type. HRs with 95% confidence intervals are shown. P values, univariable Cox proportional hazards regression. In the risk tables, the numbers represent the number of participants. Data are from combined Chowell et al., MSK1, and MSK2 sets (n = 2032 participants). Abbreviations: SCLC, small-cell lung cancer; CNS, central nervous system tumor; Unknown primary, cancer of unknown primary.

Source data

Extended Data Fig. 6 Comparison of predictive performance between the NSCLC-specific LLR6, pan-cancer LLR6, and NSCLC-specific LLR2 models.

a. Receiver operating characteristic curves and corresponding AUCs with 95% confidence intervals of the NSCLC-specific (blue curves) and pan-cancer (orange curves) LLR6 models. P values are from DeLong’s test. In the figure, ‘n’ represents the number of participants. b-c. Forest plots of HRs of PFS (b) and OS (c) within each data set using pan-cancer LORIS (binned at 0.5, which maximizes the Youden’s index on the training data) in a multivariable Cox model with adjustment for sex, age and ICB drug class. P values, multivariable Cox proportional hazards regression with adjustment for sex, age, and ICB drug class. Squares positioned at midpoints symbolize point estimates of HRs, and the accompanying bars indicate 95% confidence intervals. In the figure, the samples represent the number of participants. d. Receiver operating characteristic curves and corresponding AUCs with 95% confidence intervals of the LLR6 (blue curves) and LLR2 (orange curves) models. P values, two-tailed DeLong’s test. The LLR2 model takes two variables, that is, patient TMB and PD-L1 TPS, as the input. In the figure, ‘n’ represents the number of participants. The dashed lines in a and d represent random performance, serving as a baseline with an AUC of 0.5. This indicates the performance expected from a classifier making random guesses. e-f. Forest plots of HRs of PFS (e) and OS (f) within each data set using LLR2 LORIS (binned at 0.46, which maximizes the Youden’s index on the training data). P values, multivariable Cox proportional hazards regression with adjustment for cancer type and age. Squares positioned at midpoints symbolize point estimates of HRs, and the accompanying bars indicate 95% confidence intervals. In the figure, the samples represent the number of participants.

Source data

Extended Data Fig. 7 Comparison of predictive performance of the pan-cancer LLR6 model, the RF6 model and TMB biomarker on non-NSCLC participants.

a. Receiver operating characteristic curves and corresponding AUCs with 95% confidence intervals of LLR6 (blue curves), RF6 (green curves), and the TMB biomarker (yellow curves) on the training set and across multiple unseen test sets. In the figure, ‘n’ represents the number of participants. The dashed lines represent random performance, serving as a baseline with an AUC of 0.5. This indicates the performance expected from a classifier making random guesses. b. Distribution of LORIS, RF6 score, and TMB alone in responders and non-responders on the training set and across multiple unseen test sets. P values, two-tailed Mann–Whitney U test. Box boundaries represent the first and third quartiles; the central line marks the median. Whiskers extend to the furthest non-outlier points within 1.5 times the interquartile range. Outliers are shown as points beyond the whiskers. c-d. Kaplan–Meier analysis of OS. TMB is binned at 10 mutations per Mb and LORIS is binned at 0.5 for panel c; TMB is binned at the highest 20th percentile and LORIS is binned at the 50th percentile for each cancer type for panel d. HRs with 95% confidence intervals are shown. P values, univariable Cox proportional hazards regression. H, high; L, low. In the risk tables, the numbers represent the number of participants. Data are from combined Chowell test and MSK1 sets, with all NSCLC patients excluded from the analysis (n = 633 participants).

Source data

Extended Data Fig. 8 Monotonic relationship between pan-cancer LORIS and patient objective response probability & survival following immunotherapy among non-NSCLC participants.

a, b. Relationship between LORIS (a) or TMB (b) and ICB objective response probability. The average participant response probabilities with 95% confidence intervals are shown using 1,000-replicate bootstrapping. The grey region represents participants with an unlikely response to immunotherapy (with a response probability below 10%), while the green regions represent participants with a likely response (with a response probability exceeding 50%). The arrows indicate the LORIS and TMB threshold values. c, d. Kaplan–Meier analysis of OS. LORIS (c) and TMB (d) are binned at the different percentiles in each cancer type. P values next to the legend indicate pairwise single-tail comparisons testing against the hypothesis that ‘higher scored participants do not have better survival than lower scored participants’ with univariable Cox proportional hazards regression. HRs with 95% confidence intervals are shown for the lowest-percentile (0–10%) and the highest-percentile groups (90–100%) with univariable Cox proportional hazards regression. In the risk tables, the numbers represent the number of participants. Data are from combined Chowell test and MSK1 sets, with all NSCLC participants excluded from the analysis (n = 633 participants).

Source data

Extended Data Fig. 9 LORIS performance is maintained after removing NSCLC participants (a) or removing cancer type information (b).

a. Comparison of predictive performance among non-NSCLC participants between the original pan-cancer LLR6 model and a new LLR6 model trained without including NSCLC participants. Receiver operating characteristic curves and corresponding AUCs with 95% confidence intervals of the original pan-cancer LLR6 model (w/; blue curves) and a new LLR6 model trained without including NSCLC participants (w/o; orange curves). Number of participants in different cohorts is displayed in the figure. In the figure, ‘n’ represents the number of participants. P values, two-tailed DeLong’s test. Note that all NSCLC participants are excluded from the analysis. b. Comparison of predictive performance between the pan-cancer LLR6 model with and without the utilization of the cancer type calibration term. Receiver operating characteristic curves and corresponding AUCs with 95% confidence intervals of the original pan-cancer LLR6 model (LLR6; blue curves) and; orange curves). Number of participants in different cohorts is displayed in the figure. In the figure, ‘n’ represents the number of participants. P values, two-tailed DeLong’s test. The dashed lines represent random performance, serving as a baseline with an AUC of 0.5. This indicates the performance expected from a classifier making random guesses.

Source data

Extended Data Fig. 10 Comparison of predictive performance between the LLR6 models and the LLR5 models that exclude a patient′s systemic therapy history.

a. Receiver operating characteristic curves and corresponding AUCs with 95% confidence intervals of the pan-cancer LLR6 (blue curves) and LLR5 (orange curves) models. Number of participants in different cohorts is displayed in the figure. In the figure, ‘n’ represents the number of participants. P values, two-tailed DeLong’s test. b. Receiver operating characteristic curves and corresponding AUCs with 95% confidence intervals of the NSCLC-specific LLR6 (blue curves) and LLR5 (orange curves) models. Number of participants in different cohorts is displayed in the figure. In the figure, ‘n’ represents the number of participants. P values, two-tailed DeLong’s test.

Source data

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Tables 1–6.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 6

Statistical source data.

Source Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 9

Statistical source data.

Source Data Extended Data Fig. 10

Statistical source data.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chang, TG., Cao, Y., Sfreddo, H.J. et al. LORIS robustly predicts patient outcomes with immune checkpoint blockade therapy using common clinical, pathologic and genomic features. Nat Cancer (2024). https://doi.org/10.1038/s43018-024-00772-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s43018-024-00772-7

Search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer